Journal articles on the topic 'Weakly supervised classifier'
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Jager, M., C. Knoll, and F. A. Hamprecht. "Weakly Supervised Learning of a Classifier for Unusual Event Detection." IEEE Transactions on Image Processing 17, no. 9 (September 2008): 1700–1708. http://dx.doi.org/10.1109/tip.2008.2001043.
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Wang, Mo, Jing Wang, and Li Chen. "Mapping Paddy Rice Using Weakly Supervised Long Short-Term Memory Network with Time Series Sentinel Optical and SAR Images." Agriculture 10, no. 10 (October 19, 2020): 483. http://dx.doi.org/10.3390/agriculture10100483.
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Rice is one of the most important staple food sources worldwide. Effective and cheap monitoring of rice planting areas is demanded by many developing countries. This study proposed a weakly supervised paddy rice mapping approach based on long short-term memory (LSTM) network and dynamic time warping (DTW) distance. First, standard temporal synthetic aperture radar (SAR) backscatter profiles for each land cover type were constructed on the basis of a small number of field samples. Weak samples were then labeled on the basis of their DTW distances to the standard temporal profiles. A time series feature set was then created that combined multi-spectral Sentinel-2 bands and Sentinel-1 SAR vertical received (VV) band. With different combinations of training and testing datasets, we trained a specifically designed LSTM classifier and validated the performance of weakly supervised learning. Experiments showed that weakly supervised learning outperformed supervised learning in paddy rice identification when field samples were insufficient. With only 10% of field samples, weakly supervised learning achieved better results in producer’s accuracy (0.981 to 0.904) and user’s accuracy (0.961 to 0.917) for paddy rice. Training with 50% of field samples also presented improvement with weakly supervised learning, although not as prominent. Finally, a paddy rice map was generated with the weakly supervised approach trained on field samples and DTW-labeled samples. The proposed data labeling approach based on DTW distance can reduce field sampling cost since it requires fewer field samples. Meanwhile, validation results indicated that the proposed LSTM classifier is suitable for paddy rice mapping where variance exists in planting and harvesting schedules.
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Li Min, Song Yu,, Du Weidong, He Yujie, Gou Yao, Wu Zhaoqing, and Lv Yilong. "Strong Representation Learning for Weakly Supervised Object Detection." International Journal of Engineering and Computer Science 11, no. 02 (February 1, 2022): 25493–507. http://dx.doi.org/10.18535/ijecs/v11i02.4650.
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To solve the problem that the feature maps generated by feature extraction network of traditional weakly supervised learning object detection algorithm is not strong in feature, and the mapping relationship between feature space and classification results is not strong, which restricts the performance of object detection, a weakly supervised object detection algorithm based on strong representation learning is proposed in this paper. Due to enhance the representation ability of feature maps, the algorithm weighted the channels of feature maps according to the importance of each channel, to strengthen the weight of crucial feature maps and ignore the significance of secondary feature maps. Meanwhile, a Gaussian Mixture distribution model with better classification performance was used to design the object instance classifier to enhance further the representation of the mapping between feature space and classification results, and a large-margin Gaussian Mixture (L-GM) loss was designed to increase the distance between sample categories and improve the generalization of the classifier. For verifying the effectiveness and advancement of the proposed algorithm, the performance of the proposed algorithm is compared with six classical weakly supervised target detection algorithms on VOC datasets. Experiments show that the weakly supervised target detection algorithm based on strong representation learning has outperformed other classical algorithms in average accuracy (AP) and correct location (CorLoc), with increases of 1.1%~14.6% and 2.8%~19.4%, respectively.
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Hsu, Kuang-Jui, Yen-Yu Lin, and Yung-Yu Chuang. "Weakly Supervised Salient Object Detection by Learning A Classifier-Driven Map Generator." IEEE Transactions on Image Processing 28, no. 11 (November 2019): 5435–49. http://dx.doi.org/10.1109/tip.2019.2917224.
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Choqueluque-Roman, David, and Guillermo Camara-Chavez. "Weakly Supervised Violence Detection in Surveillance Video." Sensors 22, no. 12 (June 14, 2022): 4502. http://dx.doi.org/10.3390/s22124502.
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Automatic violence detection in video surveillance is essential for social and personal security. Monitoring the large number of surveillance cameras used in public and private areas is challenging for human operators. The manual nature of this task significantly increases the possibility of ignoring important events due to human limitations when paying attention to multiple targets at a time. Researchers have proposed several methods to detect violent events automatically to overcome this problem. So far, most previous studies have focused only on classifying short clips without performing spatial localization. In this work, we tackle this problem by proposing a weakly supervised method to detect spatially and temporarily violent actions in surveillance videos using only video-level labels. The proposed method follows a Fast-RCNN style architecture, that has been temporally extended. First, we generate spatiotemporal proposals (action tubes) leveraging pre-trained person detectors, motion appearance (dynamic images), and tracking algorithms. Then, given an input video and the action proposals, we extract spatiotemporal features using deep neural networks. Finally, a classifier based on multiple-instance learning is trained to label each action tube as violent or non-violent. We obtain similar results to the state of the art in three public databases Hockey Fight, RLVSD, and RWF-2000, achieving an accuracy of 97.3%, 92.88%, 88.7%, respectively.
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Wang, Jue, Ke Chen, Lidan Shou, Sai Wu, and Gang Chen. "Effective Slot Filling via Weakly-Supervised Dual-Model Learning." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 16 (May 18, 2021): 13952–60. http://dx.doi.org/10.1609/aaai.v35i16.17643.
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Slot filling is a challenging task in Spoken Language Understanding (SLU). Supervised methods usually require large amounts of annotation to maintain desirable performance. A solution to relieve the heavy dependency on labeled data is to employ bootstrapping, which leverages unlabeled data. However, bootstrapping is known to suffer from semantic drift. We argue that semantic drift can be tackled by exploiting the correlation between slot values (phrases) and their respective types. By using some particular weakly labeled data, namely the plain phrases included in sentences, we propose a weakly-supervised slot filling approach. Our approach trains two models, namely a classifier and a tagger, which can effectively learn from each other on the weakly labeled data. The experimental results demonstrate that our approach achieves better results than standard baselines on multiple datasets, especially in the low-resource setting.
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Kim, Beomyoung, Sangeun Han, and Junmo Kim. "Discriminative Region Suppression for Weakly-Supervised Semantic Segmentation." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 2 (May 18, 2021): 1754–61. http://dx.doi.org/10.1609/aaai.v35i2.16269.
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Weakly-supervised semantic segmentation (WSSS) using image-level labels has recently attracted much attention for reducing annotation costs. Existing WSSS methods utilize localization maps from the classification network to generate pseudo segmentation labels. However, since localization maps obtained from the classifier focus only on sparse discriminative object regions, it is difficult to generate high-quality segmentation labels. To address this issue, we introduce discriminative region suppression (DRS) module that is a simple yet effective method to expand object activation regions. DRS suppresses the attention on discriminative regions and spreads it to adjacent non-discriminative regions, generating dense localization maps. DRS requires few or no additional parameters and can be plugged into any network. Furthermore, we introduce an additional learning strategy to give a self-enhancement of localization maps, named localization map refinement learning. Benefiting from this refinement learning, localization maps are refined and enhanced by recovering some missing parts or removing noise itself. Due to its simplicity and effectiveness, our approach achieves mIoU 71.4% on the PASCAL VOC 2012 segmentation benchmark using only image-level labels. Extensive experiments demonstrate the effectiveness of our approach.
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Tai, Chang-Yu, Ming-Yao Li, and Lun-Wei Ku. "Hyperbolic Disentangled Representation for Fine-Grained Aspect Extraction." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 10 (June 28, 2022): 11358–66. http://dx.doi.org/10.1609/aaai.v36i10.21387.
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Automatic identification of salient aspects from user reviews is especially useful for opinion analysis. There has been significant progress in utilizing weakly supervised approaches, which require only a small set of seed words for training aspect classifiers. However, there is always room for improvement. First, no weakly supervised approaches fully utilize latent hierarchies between words. Second, each seed word’s representation should have different latent semantics and be distinct when it represents a different aspect. In this paper we propose HDAE, a hyperbolic disentangled aspect extractor in which a hyperbolic aspect classifier captures words’ latent hierarchies, and an aspect-disentangled representation models the distinct latent semantics of each seed word. Compared to previous baselines, HDAE achieves average F1 performance gains of 18.2% and 24.1% on Amazon product review and restaurant review datasets, respectively. In addition, the embedding visualization experience demonstrates that HDAE is a more effective approach to leveraging seed words. An ablation study and a case study further attest the effectiveness of the proposed components.
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Sun, Hao, Xuyun Fu, and Shisheng Zhong. "A Weakly Supervised Gas-Path Anomaly Detection Method for Civil Aero-Engines Based on Mapping Relationship Mining of Gas-Path Parameters and Improved Density Peak Clustering." Sensors 21, no. 13 (July 1, 2021): 4526. http://dx.doi.org/10.3390/s21134526.
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Gas-path anomalies account for more than 90% of all civil aero-engine anomalies. It is essential to develop accurate gas-path anomaly detection methods. Therefore, a weakly supervised gas-path anomaly detection method for civil aero-engines based on mapping relationship mining of gas-path parameters and improved density peak clustering is proposed. First, the encoder-decoder, composed of an attention mechanism and a long short-term memory neural network, is used to construct the mapping relationship mining model among gas-path parameters. The predicted values of gas-path parameters under the restriction of mapping relationships are obtained. The deviation degree from the original values to the predicted values is regarded as the feature. To force the extracted features to better reflect the anomalies and make full use of weakly supervised labels, a weakly supervised cross-entropy loss function under extreme class imbalance is deployed. This loss function can be combined with a simple classifier to significantly improve the feature extraction results, in which anomaly samples are more different from normal samples and do not reduce the mining precision. Finally, an anomaly detection method is deployed based on improved density peak clustering and a weakly supervised clustering parameter adjustment strategy. In the improved density peak clustering method, the local density is enhanced by K-nearest neighbors, and the clustering effect is improved by a new outlier threshold determination method and a new outlier treatment method. Through these settings, the accuracy of dividing outliers and clustering can be improved, and the influence of outliers on the clustering process reduced. By introducing weakly supervised label information and automatically iterating according to clustering and anomaly detection results to update the hyperparameter settings, a weakly supervised anomaly detection method without complex parameter adjustment processes can be implemented. The experimental results demonstrate the superiority of the proposed method.
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Guinard, S., and L. Landrieu. "WEAKLY SUPERVISED SEGMENTATION-AIDED CLASSIFICATION OF URBAN SCENES FROM 3D LIDAR POINT CLOUDS." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-1/W1 (May 31, 2017): 151–57. http://dx.doi.org/10.5194/isprs-archives-xlii-1-w1-151-2017.
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We consider the problem of the semantic classification of 3D LiDAR point clouds obtained from urban scenes when the training set is limited. We propose a non-parametric segmentation model for urban scenes composed of anthropic objects of simple shapes, partionning the scene into geometrically-homogeneous segments which size is determined by the local complexity. This segmentation can be integrated into a conditional random field classifier (CRF) in order to capture the high-level structure of the scene. For each cluster, this allows us to aggregate the noisy predictions of a weakly-supervised classifier to produce a higher confidence data term. We demonstrate the improvement provided by our method over two publicly-available large-scale data sets.
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Shen, Yiqiu, Nan Wu, Jason Phang, Jungkyu Park, Kangning Liu, Sudarshini Tyagi, Laura Heacock, et al. "An interpretable classifier for high-resolution breast cancer screening images utilizing weakly supervised localization." Medical Image Analysis 68 (February 2021): 101908. http://dx.doi.org/10.1016/j.media.2020.101908.
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Nartey, Obed Tettey, Guowu Yang, Sarpong Kwadwo Asare, Jinzhao Wu, and Lady Nadia Frempong. "Robust Semi-Supervised Traffic Sign Recognition via Self-Training and Weakly-Supervised Learning." Sensors 20, no. 9 (May 8, 2020): 2684. http://dx.doi.org/10.3390/s20092684.
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Traffic sign recognition is a classification problem that poses challenges for computer vision and machine learning algorithms. Although both computer vision and machine learning techniques have constantly been improved to solve this problem, the sudden rise in the number of unlabeled traffic signs has become even more challenging. Large data collation and labeling are tedious and expensive tasks that demand much time, expert knowledge, and fiscal resources to satisfy the hunger of deep neural networks. Aside from that, the problem of having unbalanced data also poses a greater challenge to computer vision and machine learning algorithms to achieve better performance. These problems raise the need to develop algorithms that can fully exploit a large amount of unlabeled data, use a small amount of labeled samples, and be robust to data imbalance to build an efficient and high-quality classifier. In this work, we propose a novel semi-supervised classification technique that is robust to small and unbalanced data. The framework integrates weakly-supervised learning and self-training with self-paced learning to generate attention maps to augment the training set and utilizes a novel pseudo-label generation and selection algorithm to generate and select pseudo-labeled samples. The method improves the performance by: (1) normalizing the class-wise confidence levels to prevent the model from ignoring hard-to-learn samples, thereby solving the imbalanced data problem; (2) jointly learning a model and optimizing pseudo-labels generated on unlabeled data; and (3) enlarging the training set to satisfy the hunger of deep learning models. Extensive evaluations on two public traffic sign recognition datasets demonstrate the effectiveness of the proposed technique and provide a potential solution for practical applications.
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Cholakkal, Hisham, Jubin Johnson, and Deepu Rajan. "Backtracking Spatial Pyramid Pooling-Based Image Classifier for Weakly Supervised Top–Down Salient Object Detection." IEEE Transactions on Image Processing 27, no. 12 (December 2018): 6064–78. http://dx.doi.org/10.1109/tip.2018.2864891.
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Wang, Sherrie, William Chen, Sang Michael Xie, George Azzari, and David B. Lobell. "Weakly Supervised Deep Learning for Segmentation of Remote Sensing Imagery." Remote Sensing 12, no. 2 (January 7, 2020): 207. http://dx.doi.org/10.3390/rs12020207.
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Accurate automated segmentation of remote sensing data could benefit applications from land cover mapping and agricultural monitoring to urban development surveyal and disaster damage assessment. While convolutional neural networks (CNNs) achieve state-of-the-art accuracy when segmenting natural images with huge labeled datasets, their successful translation to remote sensing tasks has been limited by low quantities of ground truth labels, especially fully segmented ones, in the remote sensing domain. In this work, we perform cropland segmentation using two types of labels commonly found in remote sensing datasets that can be considered sources of “weak supervision”: (1) labels comprised of single geotagged points and (2) image-level labels. We demonstrate that (1) a U-Net trained on a single labeled pixel per image and (2) a U-Net image classifier transferred to segmentation can outperform pixel-level algorithms such as logistic regression, support vector machine, and random forest. While the high performance of neural networks is well-established for large datasets, our experiments indicate that U-Nets trained on weak labels outperform baseline methods with as few as 100 labels. Neural networks, therefore, can combine superior classification performance with efficient label usage, and allow pixel-level labels to be obtained from image labels.
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Guha, Satarupa, Tanmoy Chakraborty, Samik Datta, Mohit Kumar, and Vasudeva Varma. "TweetGrep: Weakly Supervised Joint Retrieval and Sentiment Analysis of Topical Tweets." Proceedings of the International AAAI Conference on Web and Social Media 10, no. 1 (August 4, 2021): 161–70. http://dx.doi.org/10.1609/icwsm.v10i1.14719.
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An overwhelming amount of data is generated everyday onsocial media, encompassing a wide spectrum of topics. With almost every business decision depending on customer opinion, mining of social media data needs to be quick and easy.For a data analyst to keep up with the agility and the scale of the data, it is impossible to bank on fully supervised techniques to mine topics and their associated sentiments from social media. Motivated by this, we propose a weakly supervised approach (named, TweetGrep) that lets the data analyst easily define a topic by few keywords and adapt a generic sentiment classifier to the topic – by jointly modeling topics and sentiments using label regularization. Experiments with diverse datasets show that TweetGrep beats the state-of-the-art models for both the tasks of retrieving topical tweet sand analyzing the sentiment of the tweets (average improvement of 4.97% and 6.91% respectively in terms of area under the curve). Further, we show that TweetGrep can also be adopted in a novel task of hashtag disambiguation, which significantly outperforms the baseline methods.
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Park, Nojin, and Hanseok Ko. "Weakly Supervised Learning for Object Localization Based on an Attention Mechanism." Applied Sciences 11, no. 22 (November 19, 2021): 10953. http://dx.doi.org/10.3390/app112210953.
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Recently, deep learning has been successfully applied to object detection and localization tasks in images. When setting up deep learning frameworks for supervised training with large datasets, strongly labeling the objects facilitates good performance; however, the complexity of the image scene and large size of the dataset make this a laborious task. Hence, it is of paramount importance that the expensive work associated with the tasks involving strong labeling, such as bounding box annotation, is reduced. In this paper, we propose a method to perform object localization tasks without bounding box annotation in the training process by means of employing a two-path activation-map-based classifier framework. In particular, we develop an activation-map-based framework to judicially control the attention map in the perception branch by adding a two-feature extractor so that better attention weights can be distributed to induce improved performance. The experimental results indicate that our method surpasses the performance of the existing deep learning models based on weakly supervised object localization. The experimental results show that the proposed method achieves the best performance, with 75.21% Top-1 classification accuracy and 55.15% Top-1 localization accuracy on the CUB-200-2011 dataset.
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Banerjee, Imon, Kevin Li, Martin Seneviratne, Michelle Ferrari, Tina Seto, James D. Brooks, Daniel L. Rubin, and Tina Hernandez-Boussard. "Weakly supervised natural language processing for assessing patient-centered outcome following prostate cancer treatment." JAMIA Open 2, no. 1 (January 4, 2019): 150–59. http://dx.doi.org/10.1093/jamiaopen/ooy057.
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Abstract Background The population-based assessment of patient-centered outcomes (PCOs) has been limited by the efficient and accurate collection of these data. Natural language processing (NLP) pipelines can determine whether a clinical note within an electronic medical record contains evidence on these data. We present and demonstrate the accuracy of an NLP pipeline that targets to assess the presence, absence, or risk discussion of two important PCOs following prostate cancer treatment: urinary incontinence (UI) and bowel dysfunction (BD). Methods We propose a weakly supervised NLP approach which annotates electronic medical record clinical notes without requiring manual chart review. A weighted function of neural word embedding was used to create a sentence-level vector representation of relevant expressions extracted from the clinical notes. Sentence vectors were used as input for a multinomial logistic model, with output being either presence, absence or risk discussion of UI/BD. The classifier was trained based on automated sentence annotation depending only on domain-specific dictionaries (weak supervision). Results The model achieved an average F1 score of 0.86 for the sentence-level, three-tier classification task (presence/absence/risk) in both UI and BD. The model also outperformed a pre-existing rule-based model for note-level annotation of UI with significant margin. Conclusions We demonstrate a machine learning method to categorize clinical notes based on important PCOs that trains a classifier on sentence vector representations labeled with a domain-specific dictionary, which eliminates the need for manual engineering of linguistic rules or manual chart review for extracting the PCOs. The weakly supervised NLP pipeline showed promising sensitivity and specificity for identifying important PCOs in unstructured clinical text notes compared to rule-based algorithms. Trial registration This is a chart review study and approved by Institutional Review Board (IRB).
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Lee, Seokjin, Minhan Kim, Seunghyeon Shin, Sooyoung Park, and Youngho Jeong. "Data-Dependent Feature Extraction Method Based on Non-Negative Matrix Factorization for Weakly Supervised Domestic Sound Event Detection." Applied Sciences 11, no. 3 (January 24, 2021): 1040. http://dx.doi.org/10.3390/app11031040.
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In this paper, feature extraction methods are developed based on the non-negative matrix factorization (NMF) algorithm to be applied in weakly supervised sound event detection. Recently, the development of various features and systems have been attempted to tackle the problems of acoustic scene classification and sound event detection. However, most of these systems use data-independent spectral features, e.g., Mel-spectrogram, log-Mel-spectrum, and gammatone filterbank. Some data-dependent feature extraction methods, including the NMF-based methods, recently demonstrated the potential to tackle the problems mentioned above for long-term acoustic signals. In this paper, we further develop the recently proposed NMF-based feature extraction method to enable its application in weakly supervised sound event detection. To achieve this goal, we develop a strategy for training the frequency basis matrix using a heterogeneous database consisting of strongly- and weakly-labeled data. Moreover, we develop a non-iterative version of the NMF-based feature extraction method so that the proposed feature extraction method can be applied as a part of the model structure similar to the modern “on-the-fly” transform method for the Mel-spectrogram. To detect the sound events, the temporal basis is calculated using the NMF method and then used as a feature for the mean-teacher-model-based classifier. The results are improved for the event-wise post-processing method. To evaluate the proposed system, simulations of the weakly supervised sound event detection were conducted using the Detection and Classification of Acoustic Scenes and Events 2020 Task 4 database. The results reveal that the proposed system has F1-score performance comparable with the Mel-spectrogram and gammatonegram and exhibits 3–5% better performance than the log-Mel-spectrum and constant-Q transform.
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Zhang, Wei, Ping Tang, Thomas Corpetti, and Lijun Zhao. "WTS: A Weakly towards Strongly Supervised Learning Framework for Remote Sensing Land Cover Classification Using Segmentation Models." Remote Sensing 13, no. 3 (January 23, 2021): 394. http://dx.doi.org/10.3390/rs13030394.
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Land cover classification is one of the most fundamental tasks in the field of remote sensing. In recent years, fully supervised fully convolutional network (FCN)-based semantic segmentation models have achieved state-of-the-art performance in the semantic segmentation task. However, creating pixel-level annotations is prohibitively expensive and laborious, especially when dealing with remote sensing images. Weakly supervised learning methods from weakly labeled annotations can overcome this difficulty to some extent and achieve impressive segmentation results, but results are limited in accuracy. Inspired by point supervision and the traditional segmentation method of seeded region growing (SRG) algorithm, a weakly towards strongly (WTS) supervised learning framework is proposed in this study for remote sensing land cover classification to handle the absence of well-labeled and abundant pixel-level annotations when using segmentation models. In this framework, only several points with true class labels are required as the training set, which are much less expensive to acquire compared with pixel-level annotations through field survey or visual interpretation using high-resolution images. Firstly, they are used to train a Support Vector Machine (SVM) classifier. Once fully trained, the SVM is used to generate the initial seeded pixel-level training set, in which only the pixels with high confidence are assigned with class labels whereas others are unlabeled. They are used to weakly train the segmentation model. Then, the seeded region growing module and fully connected Conditional Random Fields (CRFs) are used to iteratively update the seeded pixel-level training set for progressively increasing pixel-level supervision of the segmentation model. Sentinel-2 remote sensing images are used to validate the proposed framework, and SVM is selected for comparison. In addition, FROM-GLC10 global land cover map is used as training reference to directly train the segmentation model. Experimental results show that the proposed framework outperforms other methods and can be highly recommended for land cover classification tasks when the pixel-level labeled datasets are insufficient by using segmentation models.
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Amyar, Amine, Romain Modzelewski, Pierre Vera, Vincent Morard, and Su Ruan. "Weakly Supervised Tumor Detection in PET Using Class Response for Treatment Outcome Prediction." Journal of Imaging 8, no. 5 (May 9, 2022): 130. http://dx.doi.org/10.3390/jimaging8050130.
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It is proven that radiomic characteristics extracted from the tumor region are predictive. The first step in radiomic analysis is the segmentation of the lesion. However, this task is time consuming and requires a highly trained physician. This process could be automated using computer-aided detection (CAD) tools. Current state-of-the-art methods are trained in a supervised learning setting, which requires a lot of data that are usually not available in the medical imaging field. The challenge is to train one model to segment different types of tumors with only a weak segmentation ground truth. In this work, we propose a prediction framework including a 3D tumor segmentation in positron emission tomography (PET) images, based on a weakly supervised deep learning method, and an outcome prediction based on a 3D-CNN classifier applied to the segmented tumor regions. The key step is to locate the tumor in 3D. We propose to (1) calculate two maximum intensity projection (MIP) images from 3D PET images in two directions, (2) classify the MIP images into different types of cancers, (3) generate the class activation maps through a multitask learning approach with a weak prior knowledge, and (4) segment the 3D tumor region from the two 2D activation maps with a proposed new loss function for the multitask. The proposed approach achieves state-of-the-art prediction results with a small data set and with a weak segmentation ground truth. Our model was tested and validated for treatment response and survival in lung and esophageal cancers on 195 patients, with an area under the receiver operating characteristic curve (AUC) of 67% and 59%, respectively, and a dice coefficient of 73% and 0.77% for tumor segmentation.
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Mirroshandel, S. A., and G. Ghassem-Sani. "Towards Unsupervised Learning of Temporal Relations between Events." Journal of Artificial Intelligence Research 45 (September 26, 2012): 125–63. http://dx.doi.org/10.1613/jair.3693.
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Automatic extraction of temporal relations between event pairs is an important task for several natural language processing applications such as Question Answering, Information Extraction, and Summarization. Since most existing methods are supervised and require large corpora, which for many languages do not exist, we have concentrated our efforts to reduce the need for annotated data as much as possible. This paper presents two different algorithms towards this goal. The first algorithm is a weakly supervised machine learning approach for classification of temporal relations between events. In the first stage, the algorithm learns a general classifier from an annotated corpus. Then, inspired by the hypothesis of "one type of temporal relation per discourse'', it extracts useful information from a cluster of topically related documents. We show that by combining the global information of such a cluster with local decisions of a general classifier, a bootstrapping cross-document classifier can be built to extract temporal relations between events. Our experiments show that without any additional annotated data, the accuracy of the proposed algorithm is higher than that of several previous successful systems. The second proposed method for temporal relation extraction is based on the expectation maximization (EM) algorithm. Within EM, we used different techniques such as a greedy best-first search and integer linear programming for temporal inconsistency removal. We think that the experimental results of our EM based algorithm, as a first step toward a fully unsupervised temporal relation extraction method, is encouraging.
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Dvořák, J., M. Potůčková, and V. Treml. "WEAKLY SUPERVISED LEARNING FOR TREELINE ECOTONE CLASSIFICATION BASED ON AERIAL ORTHOIMAGES AND AN ANCILLARY DSM." ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences V-3-2022 (May 17, 2022): 33–38. http://dx.doi.org/10.5194/isprs-annals-v-3-2022-33-2022.
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Abstract. Convolutional neural networks (CNNs) effectively classify standard datasets in remote sensing (RS). Yet, real-world data are more difficult to classify using CNNs because these networks require relatively large amounts of training data. To reduce training data requirements, two approaches can be followed – either pretraining models on larger datasets or augmenting the available training data. However, these commonly used strategies do not fully resolve the lack of training data for land cover classification in RS. Our goal is to classify trees and shrubs from aerial orthoimages in the treeline ecotone of the Krkonoše Mountains, Czechia. Instead of training a model on a smaller, human-labelled dataset, we semiautomatically created training data using an ancillary normalised Digital Surface Model (nDSM) and image spectral information. This approach can complement existing techniques, trading accuracy for a larger labelled dataset while assuming that the classifier can handle the training data noise. Weakly supervised learning on a CNN led to 68.99% mean Intersection over Union (IoU) and 81.65% mean F1-score for U-Net and 72.94% IoU and 84.35% mean F1-score for our modified U-Net on a test set comprising over 1000 manually labelled points. Notwithstanding the bias resulting from the noise in training data (especially in the least occurring tree class), our data show that standard semantic segmentation networks can be used for weakly supervised learning for local-scale land cover mapping.
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Nashaat, Mona, Aindrila Ghosh, James Miller, and Shaikh Quader. "Semi-Supervised Ensemble Learning for Dealing with Inaccurate and Incomplete Supervision." ACM Transactions on Knowledge Discovery from Data 16, no. 3 (June 30, 2022): 1–33. http://dx.doi.org/10.1145/3473910.
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In real-world tasks, obtaining a large set of noise-free data can be prohibitively expensive. Therefore, recent research tries to enable machine learning to work with weakly supervised datasets, such as inaccurate or incomplete data. However, the previous literature treats each type of weak supervision individually, although, in most cases, different types of weak supervision tend to occur simultaneously. Therefore, in this article, we present Smart MEnDR, a Classification Model that applies Ensemble Learning and Data-driven Rectification to deal with inaccurate and incomplete supervised datasets. The model first applies a preliminary phase of ensemble learning in which the noisy data points are detected while exploiting the unlabelled data. The phase employs a semi-supervised technique with maximum likelihood estimation to decide on the disagreement rate. Second, the proposed approach applies an iterative meta-learning step to tackle the problem of knowing which points should be made correct to improve the performance of the final classifier. To evaluate the proposed framework, we report the classification performance, noise detection, and the labelling accuracy of the proposed method against state-of-the-art techniques. The experimental results demonstrate the effectiveness of the proposed framework in detecting noise, providing correct labels, and attaining high classification performance.
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Gong, Yanwen, Shushrruth Sai Srinivasan, Ruiyi Zhang, Kai Kessenbrock, and Jing Zhang. "scEpiLock: A Weakly Supervised Learning Framework for cis-Regulatory Element Localization and Variant Impact Quantification for Single-Cell Epigenetic Data." Biomolecules 12, no. 7 (June 23, 2022): 874. http://dx.doi.org/10.3390/biom12070874.
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Recent advances in single-cell transposase-accessible chromatin using a sequencing assay (scATAC-seq) allow cellular heterogeneity dissection and regulatory landscape reconstruction with an unprecedented resolution. However, compared to bulk-sequencing, its ultra-high missingness remarkably reduces usable reads in each cell type, resulting in broader, fuzzier peak boundary definitions and limiting our ability to pinpoint functional regions and interpret variant impacts precisely. We propose a weakly supervised learning method, scEpiLock, to directly identify core functional regions from coarse peak labels and quantify variant impacts in a cell-type-specific manner. First, scEpiLock uses a multi-label classifier to predict chromatin accessibility via a deep convolutional neural network. Then, its weakly supervised object detection module further refines the peak boundary definition using gradient-weighted class activation mapping (Grad-CAM). Finally, scEpiLock provides cell-type-specific variant impacts within a given peak region. We applied scEpiLock to various scATAC-seq datasets and found that it achieves an area under receiver operating characteristic curve (AUC) of ~0.9 and an area under precision recall (AUPR) above 0.7. Besides, scEpiLock’s object detection condenses coarse peaks to only ⅓ of their original size while still reporting higher conservation scores. In addition, we applied scEpiLock on brain scATAC-seq data and reported several genome-wide association studies (GWAS) variants disrupting regulatory elements around known risk genes for Alzheimer’s disease, demonstrating its potential to provide cell-type-specific biological insights in disease studies.
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Pirovano, Antoine, Leandro G. Almeida, Said Ladjal, Isabelle Bloch, and Sylvain Berlemont. "Computer-aided diagnosis tool for cervical cancer screening with weakly supervised localization and detection of abnormalities using adaptable and explainable classifier." Medical Image Analysis 73 (October 2021): 102167. http://dx.doi.org/10.1016/j.media.2021.102167.
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Md Ali, Mohd Adli, Nu’man Badrud’din, Hafidzul Abdullah, and Faiz Kemi. "Alternate methods for anomaly detection in high-energy physics via semi-supervised learning." International Journal of Modern Physics A 35, no. 23 (August 12, 2020): 2050131. http://dx.doi.org/10.1142/s0217751x20501316.
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Recently, the concept of weakly supervised learning has gained popularity in the high-energy physics community due to its ability to learn even with a noisy and impure dataset. This method is valuable in the quest to discover the elusive beyond Standard Model (BSM) particle. Nevertheless, the weakly supervised learning method still requires a learning sample that describes the features of the BSM particle truthfully to the classification model. Even with the various theoretical framework such as supersymmetry and the quantum black hole, creating a BSM sample is not a trivial task since the exact feature of the particle is unknown. Due to these difficulties, we propose an alternative classifier type called the one-class classification (OCC). OCC algorithms require only background or noise samples in its training dataset, which is already abundant in the high-energy physics community. The algorithm will flag any sample that does not fit the background feature as an abnormality. In this paper, we introduce two new algorithms called EHRA and C-EHRA, which use machine learning regression and clustering to detect anomalies in samples. We tested the algorithms’ capability to create distinct anomalous patterns in the presence of BSM samples and also compare their classification output metrics to the Isolation Forest (ISF), a well-known anomaly detection algorithm. Five Monte Carlo supersymmetry datasets with the signal to noise ratio equal to 1, 0.1, 0.01, 0.001, and 0.0001 were used to test EHRA, C-EHRA and ISF algorithm. In our study, we found that the EHRA with an artificial neural network regression has the highest ROC-AUC score at 0.7882 for the balanced dataset, while the C-EHRA has the highest precision-sensitivity score for the majority of the imbalanced datasets. These findings highlight the potential use of the EHRA, C-EHRA, and other OCC algorithms in the quest to discover BSM particles.
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Sakai, Tomoya, Gang Niu, and Masashi Sugiyama. "Information-Theoretic Representation Learning for Positive-Unlabeled Classification." Neural Computation 33, no. 1 (January 2021): 244–68. http://dx.doi.org/10.1162/neco_a_01337.
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Recent advances in weakly supervised classification allow us to train a classifier from only positive and unlabeled (PU) data. However, existing PU classification methods typically require an accurate estimate of the class-prior probability, a critical bottleneck particularly for high-dimensional data. This problem has been commonly addressed by applying principal component analysis in advance, but such unsupervised dimension reduction can collapse the underlying class structure. In this letter, we propose a novel representation learning method from PU data based on the information-maximization principle. Our method does not require class-prior estimation and thus can be used as a preprocessing method for PU classification. Through experiments, we demonstrate that our method, combined with deep neural networks, highly improves the accuracy of PU class-prior estimation, leading to state-of-the-art PU classification performance.
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Niu, Tong, and Mohit Bansal. "Polite Dialogue Generation Without Parallel Data." Transactions of the Association for Computational Linguistics 6 (December 2018): 373–89. http://dx.doi.org/10.1162/tacl_a_00027.
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Stylistic dialogue response generation, with valuable applications in personality-based conversational agents, is a challenging task because the response needs to be fluent, contextually-relevant, as well as paralinguistically accurate. Moreover, parallel datasets for regular-to-stylistic pairs are usually unavailable. We present three weakly-supervised models that can generate diverse, polite (or rude) dialogue responses without parallel data. Our late fusion model (Fusion) merges the decoder of an encoder-attention-decoder dialogue model with a language model trained on stand-alone polite utterances. Our label-finetuning (LFT) model prepends to each source sequence a politeness-score scaled label (predicted by our state-of-the-art politeness classifier) during training, and at test time is able to generate polite, neutral, and rude responses by simply scaling the label embedding by the corresponding score. Our reinforcement learning model (Polite-RL) encourages politeness generation by assigning rewards proportional to the politeness classifier score of the sampled response. We also present two retrievalbased, polite dialogue model baselines. Human evaluation validates that while the Fusion and the retrieval-based models achieve politeness with poorer context-relevance, the LFT and Polite-RL models can produce significantly more polite responses without sacrificing dialogue quality.
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Choi, Hyun-Tae, and Byung-Woo Hong. "Unsupervised Object Segmentation Based on Bi-Partitioning Image Model Integrated with Classification." Electronics 10, no. 18 (September 18, 2021): 2296. http://dx.doi.org/10.3390/electronics10182296.
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The development of convolutional neural networks for deep learning has significantly contributed to image classification and segmentation areas. For high performance in supervised image segmentation, we need many ground-truth data. However, high costs are required to make these data, so unsupervised manners are actively being studied. The Mumford–Shah and Chan–Vese models are well-known unsupervised image segmentation models. However, the Mumford–Shah model and the Chan–Vese model cannot separate the foreground and background of the image because they are based on pixel intensities. In this paper, we propose a weakly supervised model for image segmentation based on the segmentation models (Mumford–Shah model and Chan–Vese model) and classification. The segmentation model (i.e., Mumford–Shah model or Chan–Vese model) is to find a base image mask for classification, and the classification network uses the mask from the segmentation models. With the classifcation network, the output mask of the segmentation model changes in the direction of increasing the performance of the classification network. In addition, the mask can distinguish the foreground and background of images naturally. Our experiment shows that our segmentation model, integrated with a classifier, can segment the input image to the foreground and the background only with the image’s class label, which is the image-level label.
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Chen, Zhanlin, Jing Zhang, Jason Liu, Yi Dai, Donghoon Lee, Martin Renqiang Min, Min Xu, and Mark Gerstein. "DECODE: a Deep-learning framework for Condensing enhancers and refining boundaries with large-scale functional assays." Bioinformatics 37, Supplement_1 (July 1, 2021): i280—i288. http://dx.doi.org/10.1093/bioinformatics/btab283.
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Abstract Motivation Mapping distal regulatory elements, such as enhancers, is a cornerstone for elucidating how genetic variations may influence diseases. Previous enhancer-prediction methods have used either unsupervised approaches or supervised methods with limited training data. Moreover, past approaches have implemented enhancer discovery as a binary classification problem without accurate boundary detection, producing low-resolution annotations with superfluous regions and reducing the statistical power for downstream analyses (e.g. causal variant mapping and functional validations). Here, we addressed these challenges via a two-step model called Deep-learning framework for Condensing enhancers and refining boundaries with large-scale functional assays (DECODE). First, we employed direct enhancer-activity readouts from novel functional characterization assays, such as STARR-seq, to train a deep neural network for accurate cell-type-specific enhancer prediction. Second, to improve the annotation resolution, we implemented a weakly supervised object detection framework for enhancer localization with precise boundary detection (to a 10 bp resolution) using Gradient-weighted Class Activation Mapping. Results Our DECODE binary classifier outperformed a state-of-the-art enhancer prediction method by 24% in transgenic mouse validation. Furthermore, the object detection framework can condense enhancer annotations to only 13% of their original size, and these compact annotations have significantly higher conservation scores and genome-wide association study variant enrichments than the original predictions. Overall, DECODE is an effective tool for enhancer classification and precise localization. Availability and implementation DECODE source code and pre-processing scripts are available at decode.gersteinlab.org. Supplementary information Supplementary data are available at Bioinformatics online.
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Pappas, Nikolaos, and Andrei Popescu-Belis. "Explicit Document Modeling through Weighted Multiple-Instance Learning." Journal of Artificial Intelligence Research 58 (March 22, 2017): 591–626. http://dx.doi.org/10.1613/jair.5240.
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Representing documents is a crucial component in many NLP tasks, for instance predicting aspect ratings in reviews. Previous methods for this task treat documents globally, and do not acknowledge that target categories are often assigned by their authors with generally no indication of the specific sentences that motivate them. To address this issue, we adopt a weakly supervised learning model, which jointly learns to focus on relevant parts of a document according to the context along with a classifier for the target categories. Derived from the weighted multiple-instance regression (MIR) framework, the model learns decomposable document vectors for each individual category and thus overcomes the representational bottleneck in previous methods due to a fixed-length document vector. During prediction, the estimated relevance or saliency weights explicitly capture the contribution of each sentence to the predicted rating, thus offering an explanation of the rating. Our model achieves state-of-the-art performance on multi-aspect sentiment analysis, improving over several baselines. Moreover, the predicted saliency weights are close to human estimates obtained by crowdsourcing, and increase the performance of lexical and topical features for review segmentation and summarization.
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Bazazian, Dena, and M. Eulàlia Parés. "EDC-Net: Edge Detection Capsule Network for 3D Point Clouds." Applied Sciences 11, no. 4 (February 19, 2021): 1833. http://dx.doi.org/10.3390/app11041833.
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Edge features in point clouds are prominent due to the capability of describing an abstract shape of a set of points. Point clouds obtained by 3D scanner devices are often immense in terms of size. Edges are essential features in large scale point clouds since they are capable of describing the shapes in down-sampled point clouds while maintaining the principal information. In this paper, we tackle challenges of edge detection tasks in 3D point clouds. To this end, we propose a novel technique to detect edges of point clouds based on a capsule network architecture. In this approach, we define the edge detection task of point clouds as a semantic segmentation problem. We built a classifier through the capsules to predict edge and non-edge points in 3D point clouds. We applied a weakly-supervised learning approach in order to improve the performance of our proposed method and built in the capability of testing the technique in wider range of shapes. We provide several quantitative and qualitative experimental results to demonstrate the robustness of our proposed EDC-Net for edge detection in 3D point clouds. We performed a statistical analysis over the ABC and ShapeNet datasets. Our numerical results demonstrate the robust and efficient performance of EDC-Net.
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Pan, Bin, Jianhao Tai, Qi Zheng, and Shanshan Zhao. "Cascade Convolutional Neural Network Based on Transfer-Learning for Aircraft Detection on High-Resolution Remote Sensing Images." Journal of Sensors 2017 (2017): 1–14. http://dx.doi.org/10.1155/2017/1796728.
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Aircraft detection from high-resolution remote sensing images is important for civil and military applications. Recently, detection methods based on deep learning have rapidly advanced. However, they require numerous samples to train the detection model and cannot be directly used to efficiently handle large-area remote sensing images. A weakly supervised learning method (WSLM) can detect a target with few samples. However, it cannot extract an adequate number of features, and the detection accuracy requires improvement. We propose a cascade convolutional neural network (CCNN) framework based on transfer-learning and geometric feature constraints (GFC) for aircraft detection. It achieves high accuracy and efficient detection with relatively few samples. A high-accuracy detection model is first obtained using transfer-learning to fine-tune pretrained models with few samples. Then, a GFC region proposal filtering method improves detection efficiency. The CCNN framework completes the aircraft detection for large-area remote sensing images. The framework first-level network is an image classifier, which filters the entire image, excluding most areas with no aircraft. The second-level network is an object detector, which rapidly detects aircraft from the first-level network output. Compared with WSLM, detection accuracy increased by 3.66%, false detection decreased by 64%, and missed detection decreased by 23.1%.
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Kazllarof, Vangjel, Stamatis Karlos, and Sotiris Kotsiantis. "Investigation of Combining Logitboost(M5P) under Active Learning Classification Tasks." Informatics 7, no. 4 (November 3, 2020): 50. http://dx.doi.org/10.3390/informatics7040050.
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Active learning is the category of partially supervised algorithms that is differentiated by its strategy to combine both the predictive ability of a base learner and the human knowledge so as to exploit adequately the existence of unlabeled data. Its ambition is to compose powerful learning algorithms which otherwise would be based only on insufficient labelled samples. Since the latter kind of information could raise important monetization costs and time obstacles, the human contribution should be seriously restricted compared with the former. For this reason, we investigate the use of the Logitboost wrapper classifier, a popular variant of ensemble algorithms which adopts the technique of boosting along with a regression base learner based on Model trees into 3 different active learning query strategies. We study its efficiency against 10 separate learners under a well-described active learning framework over 91 datasets which have been split to binary and multi-class problems. We also included one typical Logitboost variant with a separate internal regressor for discriminating the benefits of adopting a more accurate regression tree than one-node trees, while we examined the efficacy of one hyperparameter of the proposed algorithm. Since the application of the boosting technique may provide overall less biased predictions, we assume that the proposed algorithm, named as Logitboost(M5P), could provide both accurate and robust decisions under active learning scenarios that would be beneficial on real-life weakly supervised classification tasks. Its smoother weighting stage over the misclassified cases during training as well as the accurate behavior of M5P are the main factors that lead towards this performance. Proper statistical comparisons over the metric of classification accuracy verify our assumptions, while adoption of M5P instead of weak decision trees was proven to be more competitive for the majority of the examined problems. We present our results through appropriate summarization approaches and explanatory visualizations, commenting our results per case.
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Arachie, Chidubem, and Bert Huang. "Adversarial Label Learning." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 3183–90. http://dx.doi.org/10.1609/aaai.v33i01.33013183.
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We consider the task of training classifiers without labels. We propose a weakly supervised method—adversarial label learning—that trains classifiers to perform well against an adversary that chooses labels for training data. The weak supervision constrains what labels the adversary can choose. The method therefore minimizes an upper bound of the classifier’s error rate using projected primal-dual subgradient descent. Minimizing this bound protects against bias and dependencies in the weak supervision. Experiments on real datasets show that our method can train without labels and outperforms other approaches for weakly supervised learning.
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Xu, Yumo, and Mirella Lapata. "Weakly Supervised Domain Detection." Transactions of the Association for Computational Linguistics 7 (November 2019): 581–96. http://dx.doi.org/10.1162/tacl_a_00287.
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In this paper we introduce domain detection as a new natural language processing task. We argue that the ability to detect textual segments that are domain-heavy (i.e., sentences or phrases that are representative of and provide evidence for a given domain) could enhance the robustness and portability of various text classification applications. We propose an encoder-detector framework for domain detection and bootstrap classifiers with multiple instance learning. The model is hierarchically organized and suited to multilabel classification. We demonstrate that despite learning with minimal supervision, our model can be applied to text spans of different granularities, languages, and genres. We also showcase the potential of domain detection for text summarization.
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Yao, Wenlin, Cheng Zhang, Shiva Saravanan, Ruihong Huang, and Ali Mostafavi. "Weakly-Supervised Fine-Grained Event Recognition on Social Media Texts for Disaster Management." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 01 (April 3, 2020): 532–39. http://dx.doi.org/10.1609/aaai.v34i01.5391.
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People increasingly use social media to report emergencies, seek help or share information during disasters, which makes social networks an important tool for disaster management. To meet these time-critical needs, we present a weakly supervised approach for rapidly building high-quality classifiers that label each individual Twitter message with fine-grained event categories. Most importantly, we propose a novel method to create high-quality labeled data in a timely manner that automatically clusters tweets containing an event keyword and asks a domain expert to disambiguate event word senses and label clusters quickly. In addition, to process extremely noisy and often rather short user-generated messages, we enrich tweet representations using preceding context tweets and reply tweets in building event recognition classifiers. The evaluation on two hurricanes, Harvey and Florence, shows that using only 1-2 person-hours of human supervision, the rapidly trained weakly supervised classifiers outperform supervised classifiers trained using more than ten thousand annotated tweets created in over 50 person-hours.
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Lu, Zheng, and Dali Chen. "Weakly Supervised and Semi-Supervised Semantic Segmentation for Optic Disc of Fundus Image." Symmetry 12, no. 1 (January 10, 2020): 145. http://dx.doi.org/10.3390/sym12010145.
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Weakly supervised and semi-supervised semantic segmentation has been widely used in the field of computer vision. Since it does not require groundtruth or it only needs a small number of groundtruths for training. Recently, some works use pseudo groundtruths which are generated by a classified network to train the model, however, this method is not suitable for medical image segmentation. To tackle this challenging problem, we use the GrabCut method to generate the pseudo groundtruths in this paper, and then we train the network based on a modified U-net model with the generated pseudo groundtruths, finally we utilize a small amount of groundtruths to fine tune the model. Extensive experiments on the challenging RIM-ONE and DRISHTI-GS benchmarks strongly demonstrate the effectiveness of our algorithm. We obtain state-of-art results on RIM-ONE and DRISHTI-GS databases.
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Dau, Hoan Manh, Ning Xu, and Tung Khac Truong. "A Survey of Using Weakly Supervised and Semi-Supervised for Cross-Domain Sentiment Classification." Advanced Materials Research 905 (April 2014): 637–41. http://dx.doi.org/10.4028/www.scientific.net/amr.905.637.
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Supervised machine learning techniques can analyze sentiment very effectively. However, in many languages, there are few appropriate data for training sentiment classifiers. Thus, they need a large corpus of training data. In this paper, weakly-supervised techniques using a large collection of unlabeled text to determine sentiment is presented. The performance of this method maybe less depends on the domain, topic and time period represented by the testing data. In addition, semi-supervised classification using a sentiment-sensitive thesaurus is mentioned. It can be applicable when it does not have any labeled data for a target domain but have some labeled data for other multiple domains designated as the source domains. This method can learn efficiently from multiple source domains. The results show that the weakly-supervised techniques are suitable for applications requiring sentiment classification across some domains and semi-supervised techniques can learn efficiently from multiple source domains.
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Bolshina, Angelina Sergeevna, and Natalia Valentinovna Loukachevitch. "Weakly Supervised Word Sense Disambiguation Using Automatically Labelled Collections." Proceedings of the Institute for System Programming of the RAS 33, no. 6 (2021): 193–204. http://dx.doi.org/10.15514/ispras-2021-33(6)-13.
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State-of-the-art supervised word sense disambiguation models require large sense-tagged training sets. However, many low-resource languages, including Russian, lack such a large amount of data. To cope with the knowledge acquisition bottleneck in Russian, we first utilized the method based on the concept of monosemous relatives to automatically generate a labelled training collection. We then introduce three weakly supervised models trained on this synthetic data. Our work builds upon the bootstrapping approach: relying on this seed of tagged instances, the ensemble of the classifiers is used to label samples from unannotated corpora. Along with this method, different techniques were exploited to augment the new training examples. We show the simple bootstrapping approach based on the ensemble of weakly supervised models can already produce an improvement over the initial word sense disambiguation models.
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Wolf, Daniel, Sebastian Regnery, Rafal Tarnawski, Barbara Bobek-Billewicz, Joanna Polańska, and Michael Götz. "Weakly Supervised Learning with Positive and Unlabeled Data for Automatic Brain Tumor Segmentation." Applied Sciences 12, no. 21 (October 24, 2022): 10763. http://dx.doi.org/10.3390/app122110763.
Full textAbstract:
A major obstacle to the learning-based segmentation of healthy and tumorous brain tissue is the requirement of having to create a fully labeled training dataset. Obtaining these data requires tedious and error-prone manual labeling with respect to both tumor and non-tumor areas. To mitigate this problem, we propose a new method to obtain high-quality classifiers from a dataset with only small parts of labeled tumor areas. This is achieved by using positive and unlabeled learning in conjunction with a domain adaptation technique. The proposed approach leverages the tumor volume, and we show that it can be either derived with simple measures or completely automatic with a proposed estimation method. While learning from sparse samples allows reducing the necessary annotation time from 4 h to 5 min, we show that the proposed approach further reduces the necessary annotation by roughly 50% while maintaining comparative accuracies compared to traditionally trained classifiers with this approach.
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Wolf, Daniel, Sebastian Regnery, Rafal Tarnawski, Barbara Bobek-Billewicz, Joanna Polańska, and Michael Götz. "Weakly Supervised Learning with Positive and Unlabeled Data for Automatic Brain Tumor Segmentation." Applied Sciences 12, no. 21 (October 24, 2022): 10763. http://dx.doi.org/10.3390/app122110763.
Full textAbstract:
A major obstacle to the learning-based segmentation of healthy and tumorous brain tissue is the requirement of having to create a fully labeled training dataset. Obtaining these data requires tedious and error-prone manual labeling with respect to both tumor and non-tumor areas. To mitigate this problem, we propose a new method to obtain high-quality classifiers from a dataset with only small parts of labeled tumor areas. This is achieved by using positive and unlabeled learning in conjunction with a domain adaptation technique. The proposed approach leverages the tumor volume, and we show that it can be either derived with simple measures or completely automatic with a proposed estimation method. While learning from sparse samples allows reducing the necessary annotation time from 4 h to 5 min, we show that the proposed approach further reduces the necessary annotation by roughly 50% while maintaining comparative accuracies compared to traditionally trained classifiers with this approach.
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Wolf, Daniel, Sebastian Regnery, Rafal Tarnawski, Barbara Bobek-Billewicz, Joanna Polańska, and Michael Götz. "Weakly Supervised Learning with Positive and Unlabeled Data for Automatic Brain Tumor Segmentation." Applied Sciences 12, no. 21 (October 24, 2022): 10763. http://dx.doi.org/10.3390/app122110763.
Full textAbstract:
A major obstacle to the learning-based segmentation of healthy and tumorous brain tissue is the requirement of having to create a fully labeled training dataset. Obtaining these data requires tedious and error-prone manual labeling with respect to both tumor and non-tumor areas. To mitigate this problem, we propose a new method to obtain high-quality classifiers from a dataset with only small parts of labeled tumor areas. This is achieved by using positive and unlabeled learning in conjunction with a domain adaptation technique. The proposed approach leverages the tumor volume, and we show that it can be either derived with simple measures or completely automatic with a proposed estimation method. While learning from sparse samples allows reducing the necessary annotation time from 4 h to 5 min, we show that the proposed approach further reduces the necessary annotation by roughly 50% while maintaining comparative accuracies compared to traditionally trained classifiers with this approach.
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Wolf, Daniel, Sebastian Regnery, Rafal Tarnawski, Barbara Bobek-Billewicz, Joanna Polańska, and Michael Götz. "Weakly Supervised Learning with Positive and Unlabeled Data for Automatic Brain Tumor Segmentation." Applied Sciences 12, no. 21 (October 24, 2022): 10763. http://dx.doi.org/10.3390/app122110763.
Full textAbstract:
A major obstacle to the learning-based segmentation of healthy and tumorous brain tissue is the requirement of having to create a fully labeled training dataset. Obtaining these data requires tedious and error-prone manual labeling with respect to both tumor and non-tumor areas. To mitigate this problem, we propose a new method to obtain high-quality classifiers from a dataset with only small parts of labeled tumor areas. This is achieved by using positive and unlabeled learning in conjunction with a domain adaptation technique. The proposed approach leverages the tumor volume, and we show that it can be either derived with simple measures or completely automatic with a proposed estimation method. While learning from sparse samples allows reducing the necessary annotation time from 4 h to 5 min, we show that the proposed approach further reduces the necessary annotation by roughly 50% while maintaining comparative accuracies compared to traditionally trained classifiers with this approach.
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Wolf, Daniel, Sebastian Regnery, Rafal Tarnawski, Barbara Bobek-Billewicz, Joanna Polańska, and Michael Götz. "Weakly Supervised Learning with Positive and Unlabeled Data for Automatic Brain Tumor Segmentation." Applied Sciences 12, no. 21 (October 24, 2022): 10763. http://dx.doi.org/10.3390/app122110763.
Full textAbstract:
A major obstacle to the learning-based segmentation of healthy and tumorous brain tissue is the requirement of having to create a fully labeled training dataset. Obtaining these data requires tedious and error-prone manual labeling with respect to both tumor and non-tumor areas. To mitigate this problem, we propose a new method to obtain high-quality classifiers from a dataset with only small parts of labeled tumor areas. This is achieved by using positive and unlabeled learning in conjunction with a domain adaptation technique. The proposed approach leverages the tumor volume, and we show that it can be either derived with simple measures or completely automatic with a proposed estimation method. While learning from sparse samples allows reducing the necessary annotation time from 4 h to 5 min, we show that the proposed approach further reduces the necessary annotation by roughly 50% while maintaining comparative accuracies compared to traditionally trained classifiers with this approach.
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Wolf, Daniel, Sebastian Regnery, Rafal Tarnawski, Barbara Bobek-Billewicz, Joanna Polańska, and Michael Götz. "Weakly Supervised Learning with Positive and Unlabeled Data for Automatic Brain Tumor Segmentation." Applied Sciences 12, no. 21 (October 24, 2022): 10763. http://dx.doi.org/10.3390/app122110763.
Full textAbstract:
A major obstacle to the learning-based segmentation of healthy and tumorous brain tissue is the requirement of having to create a fully labeled training dataset. Obtaining these data requires tedious and error-prone manual labeling with respect to both tumor and non-tumor areas. To mitigate this problem, we propose a new method to obtain high-quality classifiers from a dataset with only small parts of labeled tumor areas. This is achieved by using positive and unlabeled learning in conjunction with a domain adaptation technique. The proposed approach leverages the tumor volume, and we show that it can be either derived with simple measures or completely automatic with a proposed estimation method. While learning from sparse samples allows reducing the necessary annotation time from 4 h to 5 min, we show that the proposed approach further reduces the necessary annotation by roughly 50% while maintaining comparative accuracies compared to traditionally trained classifiers with this approach.
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Wolf, Daniel, Sebastian Regnery, Rafal Tarnawski, Barbara Bobek-Billewicz, Joanna Polańska, and Michael Götz. "Weakly Supervised Learning with Positive and Unlabeled Data for Automatic Brain Tumor Segmentation." Applied Sciences 12, no. 21 (October 24, 2022): 10763. http://dx.doi.org/10.3390/app122110763.
Full textAbstract:
A major obstacle to the learning-based segmentation of healthy and tumorous brain tissue is the requirement of having to create a fully labeled training dataset. Obtaining these data requires tedious and error-prone manual labeling with respect to both tumor and non-tumor areas. To mitigate this problem, we propose a new method to obtain high-quality classifiers from a dataset with only small parts of labeled tumor areas. This is achieved by using positive and unlabeled learning in conjunction with a domain adaptation technique. The proposed approach leverages the tumor volume, and we show that it can be either derived with simple measures or completely automatic with a proposed estimation method. While learning from sparse samples allows reducing the necessary annotation time from 4 h to 5 min, we show that the proposed approach further reduces the necessary annotation by roughly 50% while maintaining comparative accuracies compared to traditionally trained classifiers with this approach.
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Yun, JooYeol, JungWoo Oh, and IlDong Yun. "Gradually Applying Weakly Supervised and Active Learning for Mass Detection in Breast Ultrasound Images." Applied Sciences 10, no. 13 (June 29, 2020): 4519. http://dx.doi.org/10.3390/app10134519.
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We propose a method for effectively utilizing weakly annotated image data in an object detection tasks of breast ultrasound images. Given the problem setting where a small, strongly annotated dataset and a large, weakly annotated dataset with no bounding box information are available, training an object detection model becomes a non-trivial problem. We suggest a controlled weight for handling the effect of weakly annotated images in a two stage object detection model. We also present a subsequent active learning scheme for safely assigning weakly annotated images a strong annotation using the trained model. Experimental results showed a 24% point increase in correct localization (CorLoc) measure, which is the ratio of correctly localized and classified images, by assigning the properly controlled weight. Performing active learning after a model is trained showed an additional increase in CorLoc. We tested the proposed method on the Stanford Dog datasets to assure that it can be applied to general cases, where strong annotations are insufficient to obtain resembling results. The presented method showed that higher performance is achievable with lesser annotation effort.
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Cao, Zhiyi, Shaozhang Niu, and Jiwei Zhang. "Weakly Supervised GAN for Image-to-Image Translation in the Wild." Mathematical Problems in Engineering 2020 (March 9, 2020): 1–8. http://dx.doi.org/10.1155/2020/6216048.
Full textAbstract:
Generative Adversarial Networks (GANs) have achieved significant success in unsupervised image-to-image translation between given categories (e.g., zebras to horses). Previous GANs models assume that the shared latent space between different categories will be captured from the given categories. Unfortunately, besides the well-designed datasets from given categories, many examples come from different wild categories (e.g., cats to dogs) holding special shapes and sizes (short for adversarial examples), so the shared latent space is troublesome to capture, and it will cause the collapse of these models. For this problem, we assume the shared latent space can be classified as global and local and design a weakly supervised Similar GANs (Sim-GAN) to capture the local shared latent space rather than the global shared latent space. For the well-designed datasets, the local shared latent space is close to the global shared latent space. For the wild datasets, we will get the local shared latent space to stop the model from collapse. Experiments on four public datasets show that our model significantly outperforms state-of-the-art baseline methods.
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Cai, Yingfeng, Youguo He, Hai Wang, Xiaoqiang Sun, Long Chen, and Haobin Jiang. "Pedestrian detection algorithm in traffic scene based on weakly supervised hierarchical deep model." International Journal of Advanced Robotic Systems 14, no. 1 (February 14, 2016): 172988141769231. http://dx.doi.org/10.1177/1729881417692311.
Full textAbstract:
The emergence and development of deep learning theory in machine learning field provide new method for visual-based pedestrian recognition technology. To achieve better performance in this application, an improved weakly supervised hierarchical deep learning pedestrian recognition algorithm with two-dimensional deep belief networks is proposed. The improvements are made by taking into consideration the weaknesses of structure and training methods of existing classifiers. First, traditional one-dimensional deep belief network is expanded to two-dimensional that allows image matrix to be loaded directly to preserve more information of a sample space. Then, a determination regularization term with small weight is added to the traditional unsupervised training objective function. By this modification, original unsupervised training is transformed to weakly supervised training. Subsequently, that gives the extracted features discrimination ability. Multiple sets of comparative experiments show that the performance of the proposed algorithm is better than other deep learning algorithms in recognition rate and outperforms most of the existing state-of-the-art methods in non-occlusion pedestrian data set while performs fair in weakly and heavily occlusion data set.