Relevant bibliographies by topics / Learning with noisy labels

Academic literature on the topic 'Learning with noisy labels'

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Published: 6 September 2023

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Journal articles on the topic "Learning with noisy labels"

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Xie, Ming-Kun, and Sheng-Jun Huang. "Partial Multi-Label Learning with Noisy Label Identification." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 04 (April 3, 2020): 6454–61. http://dx.doi.org/10.1609/aaai.v34i04.6117.

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Partial multi-label learning (PML) deals with problems where each instance is assigned with a candidate label set, which contains multiple relevant labels and some noisy labels. Recent studies usually solve PML problems with the disambiguation strategy, which recovers ground-truth labels from the candidate label set by simply assuming that the noisy labels are generated randomly. In real applications, however, noisy labels are usually caused by some ambiguous contents of the example. Based on this observation, we propose a partial multi-label learning approach to simultaneously recover the ground-truth information and identify the noisy labels. The two objectives are formalized in a unified framework with trace norm and ℓ1 norm regularizers. Under the supervision of the observed noise-corrupted label matrix, the multi-label classifier and noisy label identifier are jointly optimized by incorporating the label correlation exploitation and feature-induced noise model. Extensive experiments on synthetic as well as real-world data sets validate the effectiveness of the proposed approach.
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Chen, Mingcai, Hao Cheng, Yuntao Du, Ming Xu, Wenyu Jiang, and Chongjun Wang. "Two Wrongs Don’t Make a Right: Combating Confirmation Bias in Learning with Label Noise." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 12 (June 26, 2023): 14765–73. http://dx.doi.org/10.1609/aaai.v37i12.26725.

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Noisy labels damage the performance of deep networks. For robust learning, a prominent two-stage pipeline alternates between eliminating possible incorrect labels and semi-supervised training. However, discarding part of noisy labels could result in a loss of information, especially when the corruption has a dependency on data, e.g., class-dependent or instance-dependent. Moreover, from the training dynamics of a representative two-stage method DivideMix, we identify the domination of confirmation bias: pseudo-labels fail to correct a considerable amount of noisy labels, and consequently, the errors accumulate. To sufficiently exploit information from noisy labels and mitigate wrong corrections, we propose Robust Label Refurbishment (Robust LR)—a new hybrid method that integrates pseudo-labeling and confidence estimation techniques to refurbish noisy labels. We show that our method successfully alleviates the damage of both label noise and confirmation bias. As a result, it achieves state-of-the-art performance across datasets and noise types, namely CIFAR under different levels of synthetic noise and mini-WebVision and ANIMAL-10N with real-world noise.
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Li, Hui, Zhaodong Niu, Quan Sun, and Yabo Li. "Co-Correcting: Combat Noisy Labels in Space Debris Detection." Remote Sensing 14, no. 20 (October 21, 2022): 5261. http://dx.doi.org/10.3390/rs14205261.

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Space debris detection is vital to space missions and space situation awareness. Convolutional neural networks are introduced to detect space debris due to their excellent performance. However, noisy labels, caused by false alarms, exist in space debris detection, and cause ambiguous targets for the training of networks, leading to networks overfitting the noisy labels and losing the ability to detect space debris. To remedy this challenge, we introduce label-noise learning to space debris detection and propose a novel label-noise learning paradigm, termed Co-correcting, to overcome the effects of noisy labels. Co-correcting comprises two identical networks, and the predictions of these networks serve as auxiliary supervised information to mutually correct the noisy labels of their peer networks. In this manner, the effect of noisy labels can be mitigated by the mutual rectification of the two networks. Empirical experiments show that Co-correcting outperforms other state-of-the-art methods of label-noise learning, such as Co-teaching and JoCoR, in space debris detection. Even with a high label noise rate, the network trained via Co-correcting can detect space debris with high detection probability.
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Tang, Xinyu, Milad Nasr, Saeed Mahloujifar, Virat Shejwalkar, Liwei Song, Amir Houmansadr, and Prateek Mittal. "Machine Learning with Differentially Private Labels: Mechanisms and Frameworks." Proceedings on Privacy Enhancing Technologies 2022, no. 4 (October 2022): 332–50. http://dx.doi.org/10.56553/popets-2022-0112.

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Label differential privacy is a relaxation of differential privacy for machine learning scenarios where the labels are the only sensitive information that needs to be protected in the training data. For example, imagine a survey from a participant in a university class about their vaccination status. Some attributes of the students are publicly available but their vaccination status is sensitive information and must remain private. Now if we want to train a model that predicts whether a student has received vaccination using only their public information, we can use label-DP. Recent works on label-DP use different ways of adding noise to the labels in order to obtain label-DP models. In this work, we present novel techniques for training models with label-DP guarantees by leveraging unsupervised learning and semi-supervised learning, enabling us to inject less noise while obtaining the same privacy, therefore achieving a better utility-privacy trade-off. We first introduce a framework that starts with an unsupervised classifier f0 and dataset D with noisy label set Y , reduces the noise in Y using f0 , and then trains a new model f using the less noisy dataset. Our noise reduction strategy uses the model f0 to remove the noisy labels that are incorrect with high probability. Then we use semi-supervised learning to train a model using the remaining labels. We instantiate this framework with multiple ways of obtaining the noisy labels and also the base classifier. As an alternative way to reduce the noise, we explore the effect of using unsupervised learning: we only add noise to a majority voting step for associating the learned clusters with a cluster label (as opposed to adding noise to individual labels); the reduced sensitivity enables us to add less noise. Our experiments show that these techniques can significantly outperform the prior works on label-DP.
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Wu, Yichen, Jun Shu, Qi Xie, Qian Zhao, and Deyu Meng. "Learning to Purify Noisy Labels via Meta Soft Label Corrector." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 12 (May 18, 2021): 10388–96. http://dx.doi.org/10.1609/aaai.v35i12.17244.

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Recent deep neural networks (DNNs) can easily overfit to biased training data with noisy labels. Label correction strategy is commonly used to alleviate this issue by identifying suspected noisy labels and then correcting them. Current approaches to correcting corrupted labels usually need manually pre-defined label correction rules, which makes it hard to apply in practice due to the large variations of such manual strategies with respect to different problems. To address this issue, we propose a meta-learning model, aiming at attaining an automatic scheme which can estimate soft labels through meta-gradient descent step under the guidance of a small amount of noise-free meta data. By viewing the label correction procedure as a meta-process and using a meta-learner to automatically correct labels, our method can adaptively obtain rectified soft labels gradually in iteration according to current training problems. Besides, our method is model-agnostic and can be combined with any other existing classification models with ease to make it available to noisy label cases. Comprehensive experiments substantiate the superiority of our method in both synthetic and real-world problems with noisy labels compared with current state-of-the-art label correction strategies.
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Zheng, Guoqing, Ahmed Hassan Awadallah, and Susan Dumais. "Meta Label Correction for Noisy Label Learning." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 12 (May 18, 2021): 11053–61. http://dx.doi.org/10.1609/aaai.v35i12.17319.

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Leveraging weak or noisy supervision for building effective machine learning models has long been an important research problem. Its importance has further increased recently due to the growing need for large-scale datasets to train deep learning models. Weak or noisy supervision could originate from multiple sources including non-expert annotators or automatic labeling based on heuristics or user interaction signals. There is an extensive amount of previous work focusing on leveraging noisy labels. Most notably, recent work has shown impressive gains by using a meta-learned instance re-weighting approach where a meta-learning framework is used to assign instance weights to noisy labels. In this paper, we extend this approach via posing the problem as a label correction problem within a meta-learning framework. We view the label correction procedure as a meta-process and propose a new meta-learning based framework termed MLC (Meta Label Correction) for learning with noisy labels. Specifically, a label correction network is adopted as a meta-model to produce corrected labels for noisy labels while the main model is trained to leverage the corrected labels. Both models are jointly trained by solving a bi-level optimization problem. We run extensive experiments with different label noise levels and types on both image recognition and text classification tasks. We compare the re-weighing and correction approaches showing that the correction framing addresses some of the limitations of re-weighting. We also show that the proposed MLC approach outperforms previous methods in both image and language tasks.
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Shi, Jialin, Chenyi Guo, and Ji Wu. "A Hybrid Robust-Learning Architecture for Medical Image Segmentation with Noisy Labels." Future Internet 14, no. 2 (January 26, 2022): 41. http://dx.doi.org/10.3390/fi14020041.

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Deep-learning models require large amounts of accurately labeled data. However, for medical image segmentation, high-quality labels rely on expert experience, and less-experienced operators provide noisy labels. How one might mitigate the negative effects caused by noisy labels for 3D medical image segmentation has not been fully investigated. In this paper, our purpose is to propose a novel hybrid robust-learning architecture to combat noisy labels for 3D medical image segmentation. Our method consists of three components. First, we focus on the noisy annotations of slices and propose a slice-level label-quality awareness method, which automatically generates label-quality scores for slices in a set. Second, we propose a shape-awareness regularization loss based on distance transform maps to introduce prior shape information and provide extra performance gains. Third, based on a re-weighting strategy, we propose an end-to-end hybrid robust-learning architecture to weaken the negative effects caused by noisy labels. Extensive experiments are performed on two representative datasets (i.e., liver segmentation and multi-organ segmentation). Our hybrid noise-robust architecture has shown competitive performance, compared to other methods. Ablation studies also demonstrate the effectiveness of slice-level label-quality awareness and a shape-awareness regularization loss for combating noisy labels.
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Northcutt, Curtis, Lu Jiang, and Isaac Chuang. "Confident Learning: Estimating Uncertainty in Dataset Labels." Journal of Artificial Intelligence Research 70 (April 14, 2021): 1373–411. http://dx.doi.org/10.1613/jair.1.12125.

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Learning exists in the context of data, yet notions of confidence typically focus on model predictions, not label quality. Confident learning (CL) is an alternative approach which focuses instead on label quality by characterizing and identifying label errors in datasets, based on the principles of pruning noisy data, counting with probabilistic thresholds to estimate noise, and ranking examples to train with confidence. Whereas numerous studies have developed these principles independently, here, we combine them, building on the assumption of a class-conditional noise process to directly estimate the joint distribution between noisy (given) labels and uncorrupted (unknown) labels. This results in a generalized CL which is provably consistent and experimentally performant. We present sufficient conditions where CL exactly finds label errors, and show CL performance exceeding seven recent competitive approaches for learning with noisy labels on the CIFAR dataset. Uniquely, the CL framework is not coupled to a specific data modality or model (e.g., we use CL to find several label errors in the presumed error-free MNIST dataset and improve sentiment classification on text data in Amazon Reviews). We also employ CL on ImageNet to quantify ontological class overlap (e.g., estimating 645 missile images are mislabeled as their parent class projectile), and moderately increase model accuracy (e.g., for ResNet) by cleaning data prior to training. These results are replicable using the open-source cleanlab release.
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Silva, Amila, Ling Luo, Shanika Karunasekera, and Christopher Leckie. "Noise-Robust Learning from Multiple Unsupervised Sources of Inferred Labels." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 8 (June 28, 2022): 8315–23. http://dx.doi.org/10.1609/aaai.v36i8.20806.

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Deep Neural Networks (DNNs) generally require large-scale datasets for training. Since manually obtaining clean labels for large datasets is extremely expensive, unsupervised models based on domain-specific heuristics can be used to efficiently infer the labels for such datasets. However, the labels from such inferred sources are typically noisy, which could easily mislead and lessen the generalizability of DNNs. Most approaches proposed in the literature to address this problem assume the label noise depends only on the true class of an instance (i.e., class-conditional noise). However, this assumption is not realistic for the inferred labels as they are typically inferred based on the features of the instances. The few recent attempts to model such instance-dependent (i.e., feature-dependent) noise require auxiliary information about the label noise (e.g., noise rates or clean samples). This work proposes a theoretically motivated framework to correct label noise in the presence of multiple labels inferred from unsupervised models. The framework consists of two modules: (1) MULTI-IDNC, a novel approach to correct label noise that is instance-dependent yet not class-conditional; (2) MULTI-CCNC, which extends an existing class-conditional noise-robust approach to yield improved class-conditional noise correction using multiple noisy label sources. We conduct experiments using nine real-world datasets for three different classification tasks (images, text and graph nodes). Our results show that our approach achieves notable improvements (e.g., 6.4% in accuracy) against state-of-the-art baselines while dealing with both instance-dependent and class-conditional noise in inferred label sources.
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Yan, Xuguo, Xuhui Xia, Lei Wang, and Zelin Zhang. "A Progressive Deep Neural Network Training Method for Image Classification with Noisy Labels." Applied Sciences 12, no. 24 (December 12, 2022): 12754. http://dx.doi.org/10.3390/app122412754.

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Deep neural networks (DNNs) require large amounts of labeled data for model training. However, label noise is a common problem in datasets due to the difficulty of classification and high cost of labeling processes. Introducing the concepts of curriculum learning and progressive learning, this paper presents a novel solution that is able to handle massive noisy labels and improve model generalization ability. It proposes a new network model training strategy that considers mislabeled samples directly in the network training process. The new learning curriculum is designed to measures the complexity of the data with their distribution density in a feature space. The sample data in each category are then divided into easy-to-classify (clean samples), relatively easy-to-classify, and hard-to-classify (noisy samples) subsets according to the smallest intra-class local density with each cluster. On this basis, DNNs are trained progressively in three stages, from easy to hard, i.e., from clean to noisy samples. The experimental results demonstrate that the accuracy of image classification can be improved through data augmentation, and the classification accuracy of the proposed method is clearly higher than that of standard Inception_v2 for the NEU dataset after data augmentation, when the proportion of noisy labels in the training set does not exceed 60%. With 50% noisy labels in the training set, the classification accuracy of the proposed method outperformed recent state-of-the-art label noise learning methods, CleanNet and MentorNet. The proposed method also performed well in practical applications, where the number of noisy labels was uncertain and unevenly distributed. In this case, the proposed method not only can alleviate the adverse effects of noisy labels, but it can also improve the generalization ability of standard deep networks and their overall capability.
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Dissertations / Theses on the topic "Learning with noisy labels"

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Yu, Xiyu. "Learning with Biased and Noisy Labels." Thesis, The University of Sydney, 2019. http://hdl.handle.net/2123/20125.

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Recent advances in Artificial Intelligence (AI) have been built on large scale datasets. These advances often come with increasing demands on labeling, which are expensive and time consuming. Therefore, AI tends to develop its higher-level intelligence like human to capture knowledge from cheap but weak supervision such as the mislabeled data. However, current AI suffers from severely degraded performance on noisily labeled data. Thus, it is a compelling demand to design novel algorithms to enable AI to learn from noisy labels. Label noise methods such as robust loss functions assume that a fraction of data is correctly labeled to ensure effective learning. When all labels are incorrect, they often fail due to severe bias and noises. Here, we consider a kind of incorrect label, complementary label which specify a class that a feature do not belong to. We propose a general method to modify loss functions such that the classifier learned from biased complementary labels can be identical to the optimal one learned from true labels. Another challenge in label noise is the shift between distributions of training (source) and test (target) data. Existing methods often ignore these changes and they cannot learn transferable knowledge across domains. Therefore, we propose a novel Denoising Conditional Invariant Component framework which provably ensures identification of invariant representations and label distribution of target data given examples with noisy labels in source domain and unlabeled examples in target domain. Finally, we study how to estimate the noise rates in label noise. Previous methods deliver promising results but rely on strong assumptions. We can see, noise rate estimation is essentially a mixture proportion estimation problem. We also prove that noise rates can be uniquely identified and efficiently obtained under a weaker linear independent assumption.
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Caye, Daudt Rodrigo. "Convolutional neural networks for change analysis in earth observation images with noisy labels and domain shifts." Electronic Thesis or Diss., Institut polytechnique de Paris, 2020. http://www.theses.fr/2020IPPAT033.

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L'analyse de l'imagerie satellitaire et aérienne d'observation de la Terre nous permet d'obtenir des informations précises sur de vastes zones. Une analyse multitemporelle de telles images est nécessaire pour comprendre l'évolution de ces zones. Dans cette thèse, les réseaux de neurones convolutifs sont utilisés pour détecter et comprendre les changements en utilisant des images de télédétection provenant de diverses sources de manière supervisée et faiblement supervisée. Des architectures siamoises sont utilisées pour comparer des paires d'images recalées et identifier les pixels correspondant à des changements. La méthode proposée est ensuite étendue à une architecture de réseau multitâche qui est utilisée pour détecter les changements et effectuer une cartographie automatique simultanément, ce qui permet une compréhension sémantique des changements détectés. Ensuite, un filtrage de classification et un nouvel algorithme de diffusion anisotrope guidée sont utilisés pour réduire l'effet du bruit d'annotation, un défaut récurrent pour les ensembles de données à grande échelle générés automatiquement. Un apprentissage faiblement supervisé est également réalisé pour effectuer une détection de changement au niveau des pixels en utilisant uniquement une supervision au niveau de l'image grâce à l'utilisation de cartes d'activation de classe et d'une nouvelle couche d'attention spatiale. Enfin, une méthode d'adaptation de domaine fondée sur un entraînement adverse est proposée. Cette méthode permet de projeter des images de différents domaines dans un espace latent commun où une tâche donnée peut être effectuée. Cette méthode est testée non seulement pour l'adaptation de domaine pour la détection de changement, mais aussi pour la classification d'images et la segmentation sémantique, ce qui prouve sa polyvalence
The analysis of satellite and aerial Earth observation images allows us to obtain precise information over large areas. A multitemporal analysis of such images is necessary to understand the evolution of such areas. In this thesis, convolutional neural networks are used to detect and understand changes using remote sensing images from various sources in supervised and weakly supervised settings. Siamese architectures are used to compare coregistered image pairs and to identify changed pixels. The proposed method is then extended into a multitask network architecture that is used to detect changes and perform land cover mapping simultaneously, which permits a semantic understanding of the detected changes. Then, classification filtering and a novel guided anisotropic diffusion algorithm are used to reduce the effect of biased label noise, which is a concern for automatically generated large-scale datasets. Weakly supervised learning is also achieved to perform pixel-level change detection using only image-level supervision through the usage of class activation maps and a novel spatial attention layer. Finally, a domain adaptation method based on adversarial training is proposed, which succeeds in projecting images from different domains into a common latent space where a given task can be performed. This method is tested not only for domain adaptation for change detection, but also for image classification and semantic segmentation, which proves its versatility
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Fang, Tongtong. "Learning from noisy labelsby importance reweighting: : a deep learning approach." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-264125.

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Noisy labels could cause severe degradation to the classification performance. Especially for deep neural networks, noisy labels can be memorized and lead to poor generalization. Recently label noise robust deep learning has outperformed traditional shallow learning approaches in handling complex input data without prior knowledge of label noise generation. Learning from noisy labels by importance reweighting is well-studied. Existing work in this line using deep learning failed to provide reasonable importance reweighting criterion and thus got undesirable experimental performances. Targeting this knowledge gap and inspired by domain adaptation, we propose a novel label noise robust deep learning approach by importance reweighting. Noisy labeled training examples are weighted by minimizing the maximum mean discrepancy between the loss distributions of noisy labeled and clean labeled data. In experiments, the proposed approach outperforms other baselines. Results show a vast research potential of applying domain adaptation in label noise problem by bridging the two areas. Moreover, the proposed approach potentially motivate other interesting problems in domain adaptation by enabling importance reweighting to be used in deep learning.
Felaktiga annoteringar kan sänka klassificeringsprestanda.Speciellt för djupa nätverk kan detta leda till dålig generalisering. Nyligen har brusrobust djup inlärning överträffat andra inlärningsmetoder när det gäller hantering av komplexa indata Befintligta resultat från djup inlärning kan dock inte tillhandahålla rimliga viktomfördelningskriterier. För att hantera detta kunskapsgap och inspirerat av domänanpassning föreslår vi en ny robust djup inlärningsmetod som använder omviktning. Omviktningen görs genom att minimera den maximala medelavvikelsen mellan förlustfördelningen av felmärkta och korrekt märkta data. I experiment slår den föreslagna metoden andra metoder. Resultaten visar en stor forskningspotential för att tillämpa domänanpassning. Dessutom motiverar den föreslagna metoden undersökningar av andra intressanta problem inom domänanpassning genom att möjliggöra smarta omviktningar.
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Ainapure, Abhijeet Narhar. "Application and Performance Enhancement of Intelligent Cross-Domain Fault Diagnosis in Rotating Machinery." University of Cincinnati / OhioLINK, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1623164772153736.

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Chan, Jeffrey (Jeffrey D. ). "On boosting and noisy labels." Thesis, Massachusetts Institute of Technology, 2015. http://hdl.handle.net/1721.1/100297.

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Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2015.
This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
Cataloged from student-submitted PDF version of thesis.
Includes bibliographical references (pages 53-56).
Boosting is a machine learning technique widely used across many disciplines. Boosting enables one to learn from labeled data in order to predict the labels of unlabeled data. A central property of boosting instrumental to its popularity is its resistance to overfitting. Previous experiments provide a margin-based explanation for this resistance to overfitting. In this thesis, the main finding is that boosting's resistance to overfitting can be understood in terms of how it handles noisy (mislabeled) points. Confirming experimental evidence emerged from experiments using the Wisconsin Diagnostic Breast Cancer(WDBC) dataset commonly used in machine learning experiments. A majority vote ensemble filter identified on average that 2.5% of the points in the dataset as noisy. The experiments chiefly investigated boosting's treatment of noisy points from a volume-based perspective. While the cell volume surrounding noisy points did not show a significant difference from other points, the decision volume surrounding noisy points was two to three times less than that of non-noisy points. Additional findings showed that decision volume not only provides insight into boosting's resistance to overfitting in the context of noisy points, but also serves as a suitable metric for identifying which points in a dataset are likely to be mislabeled.
by Jeffrey Chan.
M. Eng.
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Almansour, Amal. "Credibility assessment for Arabic micro-blogs using noisy labels." Thesis, King's College London (University of London), 2016. https://kclpure.kcl.ac.uk/portal/en/theses/credibility-assessment-for-arabic-microblogs-using-noisy-labels(6baf983a-940d-4c2c-8821-e992348b4097).html.

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Due to their openness and low publishing barrier nature, User-Generated Content (UGC) platforms facilitate the creation of huge amounts of data, containing a substantial quantity of inaccurate content. The presence of misleading, questionable and inaccurate content may have detrimental effects on people's beliefs and decision-making and may create a public disturbance. Consequently, there is significant need to evaluate information coming from UGC platforms to differentiate credible information from misinformation and rumours. In this thesis, we present the need for research about online Arabic information credibility and argue that by extending the existing automated credibility assessment approaches to adding an extra step to evaluate labellers will lead to a more robust dataset for building the credibility classification model. This research focuses on modelling the credibility of Arabic information in the presence of disagreed judging credibility scores and ground truth of credibility information is not absolute. First, in order to achieve the stated goal, this study employs the idea of crowdsourcing whereby users can explicitly express their opinions about the credibility of a set of tweet messages. This information coupled with the data about tweets’ features enables us to identify messages’ prominent features with the highest usage in determining information credibility levels. Then experiments based on both statistical analysis using features’ distributions and machine learning methods are performed to predict and classify messages’ credibility levels. A novel credibility assessment model which integrates the labellers’ reliability weights is proposed when deriving the credibility labels for the messages in the training and testing dataset. This credibility model primarily uses similarity and accuracy rating measurements for evaluating the weighting of labellers. In order to evaluate proposed model, we compare the labelling obtained from the expert labellers with those from the weighted crowd labellers. Empirical evidence proposed that the credibility model is superior to the commonly used majority voting baseline compared to the experts’ rating evaluations. The observed experimental results exhibit a reduction of the effect of unreliable labellers’ credibility judgments and a moderate enhancement of the credibility classification results.
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Northcutt, Curtis George. "Classification with noisy labels : "Multiple Account" cheating detection in Open Online Courses." Thesis, Massachusetts Institute of Technology, 2017. http://hdl.handle.net/1721.1/111870.

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Thesis: S.M., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2017.
This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
Cataloged from student-submitted PDF version of thesis.
Includes bibliographical references (pages 113-122).
Massive Open Online Courses (MOOCs) have the potential to enhance socioeconomic mobility through education. Yet, the viability of this outcome largely depends on the reputation of MOOC certificates as a credible academic credential. I describe a cheating strategy that threatens this reputation and holds the potential to render the MOOC certificate valueless. The strategy, Copying Answers using Multiple Existences Online (CAMEO), involves a user who gathers solutions to assessment questions using one or more harvester accounts and then submits correct answers using one or more separate master accounts. To estimate a lower bound for CAMEO prevalence among 1.9 million course participants in 115 HarvardX and MITx courses, I introduce a filter-based CAMEO detection algorithm and use a small-scale experiment to verify CAMEO use with certainty. I identify preventive strategies that can decrease CAMEO rates and show evidence of their effectiveness in science courses. Because the CAMEO algorithm functions as a lower bound estimate, it fails to detect many CAMEO cheaters. As a novelty of this thesis, instead of improving the shortcomings of the CAMEO algorithm directly, I recognize that we can think of the CAMEO algorithm as a method for producing noisy predicted cheating labels. Then a solution to the more general problem of binary classification with noisy labels ( ~ P̃̃̃ Ñ learning) is a solution to CAMEO cheating detection. ~ P̃ Ñ learning is the problem of binary classification when training examples may be mislabeled (flipped) uniformly with noise rate 1 for positive examples and 0 for negative examples. I propose Rank Pruning to solve ~ P ~N learning and the open problem of estimating the noise rates. Unlike prior solutions, Rank Pruning is efficient and general, requiring O(T) for any unrestricted choice of probabilistic classifier with T fitting time. I prove Rank Pruning achieves consistent noise estimation and equivalent expected risk as learning with uncorrupted labels in ideal conditions, and derive closed-form solutions when conditions are non-ideal. Rank Pruning achieves state-of-the-art noise rate estimation and F1, error, and AUC-PR on the MNIST and CIFAR datasets, regardless of noise rates. To highlight, Rank Pruning with a CNN classifier can predict if a MNIST digit is a one or not one with only 0:25% error, and 0:46% error across all digits, even when 50% of positive examples are mislabeled and 50% of observed positive labels are mislabeled negative examples. Rank Pruning achieves similarly impressive results when as large as 50% of training examples are actually just noise drawn from a third distribution. Together, the CAMEO and Rank Pruning algorithms allow for a robust, general, and time-efficient solution to the CAMEO cheating detection problem. By ensuring the validity of MOOC credentials, we enable MOOCs to achieve both openness and value, and thus take one step closer to the greater goal of democratization of education.
by Curtis George Northcutt.
S.M.
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Ekambaram, Rajmadhan. "Active Cleaning of Label Noise Using Support Vector Machines." Scholar Commons, 2017. http://scholarcommons.usf.edu/etd/6830.

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Large scale datasets collected using non-expert labelers are prone to labeling errors. Errors in the given labels or label noise affect the classifier performance, classifier complexity, class proportions, etc. It may be that a relatively small, but important class needs to have all its examples identified. Typical solutions to the label noise problem involve creating classifiers that are robust or tolerant to errors in the labels, or removing the suspected examples using machine learning algorithms. Finding the label noise examples through a manual review process is largely unexplored due to the cost and time factors involved. Nevertheless, we believe it is the only way to create a label noise free dataset. This dissertation proposes a solution exploiting the characteristics of the Support Vector Machine (SVM) classifier and the sparsity of its solution representation to identify uniform random label noise examples in a dataset. Application of this method is illustrated with problems involving two real-world large scale datasets. This dissertation also presents results for datasets that contain adversarial label noise. A simple extension of this method to a semi-supervised learning approach is also presented. The results show that most mislabels are quickly and effectively identified by the approaches developed in this dissertation.
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Balasubramanian, Krishnakumar. "Learning without labels and nonnegative tensor factorization." Thesis, Georgia Institute of Technology, 2010. http://hdl.handle.net/1853/33926.

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Supervised learning tasks like building a classifier, estimating the error rate of the predictors, are typically performed with labeled data. In most cases, obtaining labeled data is costly as it requires manual labeling. On the other hand, unlabeled data is available in abundance. In this thesis, we discuss methods to perform supervised learning tasks with no labeled data. We prove consistency of the proposed methods and demonstrate its applicability with synthetic and real world experiments. In some cases, small quantities of labeled data maybe easily available and supplemented with large quantities of unlabeled data (semi-supervised learning). We derive the asymptotic efficiency of generative models for semi-supervised learning and quantify the effect of labeled and unlabeled data on the quality of the estimate. Another independent track of the thesis is efficient computational methods for nonnegative tensor factorization (NTF). NTF provides the user with rich modeling capabilities but it comes with an added computational cost. We provide a fast algorithm for performing NTF using a modified active set method called block principle pivoting method and demonstrate its applicability to social network analysis and text mining.
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Nugyen, Duc Tam [Verfasser], and Thomas [Akademischer Betreuer] Brox. "Robust deep learning for computer vision to counteract data scarcity and label noise." Freiburg : Universität, 2020. http://d-nb.info/1226657060/34.

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Books on the topic "Learning with noisy labels"

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Ramsay, Carol A. Pesticides: Learning about labels. [Pullman]: Cooperative Extension, Washington State University, 1999.

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Zamzmi, Ghada, Sameer Antani, Ulas Bagci, Marius George Linguraru, Sivaramakrishnan Rajaraman, and Zhiyun Xue, eds. Medical Image Learning with Limited and Noisy Data. Cham: Springer Nature Switzerland, 2022. http://dx.doi.org/10.1007/978-3-031-16760-7.

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Allan, John A. B. 1941-, ed. Kids with labels: Keys to intimacy. Toronto: Lugus Productions, 1990.

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Literacy, not labels: Celebrating students' strengths through whole language. Portsmouth, NH: Boynton/Cook Publishers, 1995.

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Wang, Qian, Fausto Milletari, Hien V. Nguyen, Shadi Albarqouni, M. Jorge Cardoso, Nicola Rieke, Ziyue Xu, et al., eds. Domain Adaptation and Representation Transfer and Medical Image Learning with Less Labels and Imperfect Data. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-33391-1.

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Carneiro, Gustavo. Machine Learning with Noisy Labels: Definitions, Theory, Techniques and Solutions. Elsevier Science & Technology Books, 2024.

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Herzog, Joyce. Learning in Spite of Labels. JoyceHerzog.com, Inc., 1994.

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Owen, Phillip. What-If... ?: Learning Without Labels. Independently Published, 2017.

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Publishing, Carson-Dellosa. Celebrate Learning Labels and Organizers. Carson-Dellosa Publishing, LLC, 2018.

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National Education Trust (Great Britain) Staff and Marc Rowland. Learning Without Labels: Improving Outcomes for Vulnerable Pupils. Catt Educational, Limited, John, 2017.

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Book chapters on the topic "Learning with noisy labels"

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Vembu, Shankar, and Sandra Zilles. "Interactive Learning from Multiple Noisy Labels." In Machine Learning and Knowledge Discovery in Databases, 493–508. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-46128-1_31.

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Goryunova, Natalya, Artem Baklanov, and Egor Ianovski. "A Noisy-Labels Approach to Detecting Uncompetitive Auctions." In Machine Learning, Optimization, and Data Science, 185–200. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-95467-3_15.

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Yang, Longrong, Fanman Meng, Hongliang Li, Qingbo Wu, and Qishang Cheng. "Learning with Noisy Class Labels for Instance Segmentation." In Computer Vision – ECCV 2020, 38–53. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-58568-6_3.

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Hu, Mengying, Hu Han, Shiguang Shan, and Xilin Chen. "Multi-label Learning from Noisy Labels with Non-linear Feature Transformation." In Computer Vision – ACCV 2018, 404–19. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-20873-8_26.

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Gao, Zhengqi, Fan-Keng Sun, Mingran Yang, Sucheng Ren, Zikai Xiong, Marc Engeler, Antonio Burazer, Linda Wildling, Luca Daniel, and Duane S. Boning. "Learning from Multiple Annotator Noisy Labels via Sample-Wise Label Fusion." In Lecture Notes in Computer Science, 407–22. Cham: Springer Nature Switzerland, 2022. http://dx.doi.org/10.1007/978-3-031-20053-3_24.

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Nigam, Nitika, Tanima Dutta, and Hari Prabhat Gupta. "Impact of Noisy Labels in Learning Techniques: A Survey." In Advances in Data and Information Sciences, 403–11. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-0694-9_38.

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Chen, Yipeng, Xiaojuan Ban, and Ke Xu. "Combating Noisy Labels via Contrastive Learning with Challenging Pairs." In Pattern Recognition and Computer Vision, 614–25. Cham: Springer Nature Switzerland, 2022. http://dx.doi.org/10.1007/978-3-031-18910-4_49.

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Liang, Xuefeng, Longshan Yao, and XingYu Liu. "Noisy Label Learning in Deep Learning." In IFIP Advances in Information and Communication Technology, 84–97. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-14903-0_10.

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Sousa, Vitor, Amanda Lucas Pereira, Manoela Kohler, and Marco Pacheco. "Learning by Small Loss Approach Multi-label to Deal with Noisy Labels." In Computational Science and Its Applications – ICCSA 2023, 385–403. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-36805-9_26.

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Cai, Zhuotong, Jingmin Xin, Peiwen Shi, Sanping Zhou, Jiayi Wu, and Nanning Zheng. "Meta Pixel Loss Correction for Medical Image Segmentation with Noisy Labels." In Medical Image Learning with Limited and Noisy Data, 32–41. Cham: Springer Nature Switzerland, 2022. http://dx.doi.org/10.1007/978-3-031-16760-7_4.

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Conference papers on the topic "Learning with noisy labels"

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Liu, Yun-Peng, Ning Xu, Yu Zhang, and Xin Geng. "Label Distribution for Learning with Noisy Labels." In Twenty-Ninth International Joint Conference on Artificial Intelligence and Seventeenth Pacific Rim International Conference on Artificial Intelligence {IJCAI-PRICAI-20}. California: International Joint Conferences on Artificial Intelligence Organization, 2020. http://dx.doi.org/10.24963/ijcai.2020/356.

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The performances of deep neural networks (DNNs) crucially rely on the quality of labeling. In some situations, labels are easily corrupted, and therefore some labels become noisy labels. Thus, designing algorithms that deal with noisy labels is of great importance for learning robust DNNs. However, it is difficult to distinguish between clean labels and noisy labels, which becomes the bottleneck of many methods. To address the problem, this paper proposes a novel method named Label Distribution based Confidence Estimation (LDCE). LDCE estimates the confidence of the observed labels based on label distribution. Then, the boundary between clean labels and noisy labels becomes clear according to confidence scores. To verify the effectiveness of the method, LDCE is combined with the existing learning algorithm to train robust DNNs. Experiments on both synthetic and real-world datasets substantiate the superiority of the proposed algorithm against state-of-the-art methods.
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Lu, Yangdi, and Wenbo He. "SELC: Self-Ensemble Label Correction Improves Learning with Noisy Labels." In Thirty-First International Joint Conference on Artificial Intelligence {IJCAI-22}. California: International Joint Conferences on Artificial Intelligence Organization, 2022. http://dx.doi.org/10.24963/ijcai.2022/455.

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Deep neural networks are prone to overfitting noisy labels, resulting in poor generalization performance. To overcome this problem, we present a simple and effective method self-ensemble label correction (SELC) to progressively correct noisy labels and refine the model. We look deeper into the memorization behavior in training with noisy labels and observe that the network outputs are reliable in the early stage. To retain this reliable knowledge, SELC uses ensemble predictions formed by an exponential moving average of network outputs to update the original noisy labels. We show that training with SELC refines the model by gradually reducing supervision from noisy labels and increasing supervision from ensemble predictions. Despite its simplicity, compared with many state-of-the-art methods, SELC obtains more promising and stable results in the presence of class-conditional, instance-dependent, and real-world label noise. The code is available at https://github.com/MacLLL/SELC.
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Li, Ziwei, Gengyu Lyu, and Songhe Feng. "Partial Multi-Label Learning via Multi-Subspace Representation." In Twenty-Ninth International Joint Conference on Artificial Intelligence and Seventeenth Pacific Rim International Conference on Artificial Intelligence {IJCAI-PRICAI-20}. California: International Joint Conferences on Artificial Intelligence Organization, 2020. http://dx.doi.org/10.24963/ijcai.2020/362.

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Partial Multi-Label Learning (PML) aims to learn from the training data where each instance is associated with a set of candidate labels, among which only a part of them are relevant. Existing PML methods mainly focus on label disambiguation, while they lack the consideration of noise in the feature space. To tackle the problem, we propose a novel framework named partial multi-label learning via MUlti-SubspacE Representation (MUSER), where the redundant labels together with noisy features are jointly taken into consideration during the training process. Specifically, we first decompose the original label space into a latent label subspace and a label correlation matrix to reduce the negative effects of redundant labels, then we utilize the correlations among features to project the original noisy feature space to a feature subspace to resist the noisy feature information. Afterwards, we introduce a graph Laplacian regularization to constrain the label subspace to keep intrinsic structure among features and impose an orthogonality constraint on the correlations among features to guarantee discriminability of the feature subspace. Extensive experiments conducted on various datasets demonstrate the superiority of our proposed method.
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Hu, Chuanyang, Shipeng Yan, Zhitong Gao, and Xuming He. "MILD: Modeling the Instance Learning Dynamics for Learning with Noisy Labels." In Thirty-Second International Joint Conference on Artificial Intelligence {IJCAI-23}. California: International Joint Conferences on Artificial Intelligence Organization, 2023. http://dx.doi.org/10.24963/ijcai.2023/92.

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Despite deep learning has achieved great success, it often relies on a large amount of training data with accurate labels, which are expensive and time-consuming to collect. A prominent direction to reduce the cost is to learn with noisy labels, which are ubiquitous in the real-world applications. A critical challenge for such a learning task is to reduce the effect of network memorization on the falsely-labeled data. In this work, we propose an iterative selection approach based on the Weibull mixture model, which identifies clean data by considering the overall learning dynamics of each data instance. In contrast to the previous small-loss heuristics, we leverage the observation that deep network is easy to memorize and hard to forget clean data. In particular, we measure the difficulty of memorization and forgetting for each instance via the transition times between being misclassified and being memorized in training, and integrate them into a novel metric for selection. Based on the proposed metric, we retain a subset of identified clean data and repeat the selection procedure to iteratively refine the clean subset, which is finally used for model training. To validate our method, we perform extensive experiments on synthetic noisy datasets and real-world web data, and our strategy outperforms existing noisy-label learning methods.
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Li, Ximing, and Yang Wang. "Recovering Accurate Labeling Information from Partially Valid Data for Effective Multi-Label Learning." In Twenty-Ninth International Joint Conference on Artificial Intelligence and Seventeenth Pacific Rim International Conference on Artificial Intelligence {IJCAI-PRICAI-20}. California: International Joint Conferences on Artificial Intelligence Organization, 2020. http://dx.doi.org/10.24963/ijcai.2020/191.

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Partial Multi-label Learning (PML) aims to induce the multi-label predictor from datasets with noisy supervision, where each training instance is associated with several candidate labels but only partially valid. To address the noisy issue, the existing PML methods basically recover the ground-truth labels by leveraging the ground-truth confidence of the candidate label, i.e., the likelihood of a candidate label being a ground-truth one. However, they neglect the information from non-candidate labels, which potentially contributes to the ground-truth label recovery. In this paper, we propose to recover the ground-truth labels, i.e., estimating the ground-truth confidences, from the label enrichment, composed of the relevance degrees of candidate labels and irrelevance degrees of non-candidate labels. Upon this observation, we further develop a novel two-stage PML method, namely Partial Multi-Label Learning with Label Enrichment-Recovery (PML3ER), where in the first stage, it estimates the label enrichment with unconstrained label propagation, then jointly learns the ground-truth confidence and multi-label predictor given the label enrichment. Experimental results validate that PML3ER outperforms the state-of-the-art PML methods.
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Gao, Ziyang, Yaping Yan, and Xin Geng. "Learning from Noisy Labels via Meta Credible Label Elicitation." In 2022 IEEE International Conference on Image Processing (ICIP). IEEE, 2022. http://dx.doi.org/10.1109/icip46576.2022.9897577.

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Tu, Yuanpeng, Boshen Zhang, Yuxi Li, Liang Liu, Jian Li, Yabiao Wang, Chengjie Wang, and Cai Rong Zhao. "Learning from Noisy Labels with Decoupled Meta Label Purifier." In 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2023. http://dx.doi.org/10.1109/cvpr52729.2023.01909.

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Zhang, Haoyu, Dingkun Long, Guangwei Xu, Muhua Zhu, Pengjun Xie, Fei Huang, and Ji Wang. "Learning with Noise: Improving Distantly-Supervised Fine-grained Entity Typing via Automatic Relabeling." In Twenty-Ninth International Joint Conference on Artificial Intelligence and Seventeenth Pacific Rim International Conference on Artificial Intelligence {IJCAI-PRICAI-20}. California: International Joint Conferences on Artificial Intelligence Organization, 2020. http://dx.doi.org/10.24963/ijcai.2020/527.

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Fine-grained entity typing (FET) is a fundamental task for various entity-leveraging applications. Although great success has been made, existing systems still have challenges in handling noisy samples in training data introduced by distant supervision methods. To address these noise, previous studies either focus on processing the clean samples (i,e., have only one label) and noisy samples (i,e., have multiple labels) with different strategies or filtering the noisy labels based on the assumption that the distantly-supervised label set certainly contains the correct type label. In this paper, we propose a probabilistic automatic relabeling method which treats all training samples uniformly. Our method aims to estimate the pseudo-truth label distribution of each sample, and the pseudo-truth distribution will be treated as part of trainable parameters which are jointly updated during the training process. The proposed approach does not rely on any prerequisite or extra supervision, making it effective on real applications. Experiments on several benchmarks show that our method outperforms previous approaches and alleviates the noisy labeling problem.
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Luo, Yijing, Bo Han, and Chen Gong. "A Bi-level Formulation for Label Noise Learning with Spectral Cluster Discovery." In Twenty-Ninth International Joint Conference on Artificial Intelligence and Seventeenth Pacific Rim International Conference on Artificial Intelligence {IJCAI-PRICAI-20}. California: International Joint Conferences on Artificial Intelligence Organization, 2020. http://dx.doi.org/10.24963/ijcai.2020/361.

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Practically, we often face the dilemma that some of the examples for training a classifier are incorrectly labeled due to various subjective and objective factors. Although intensive efforts have been put to design classifiers that are robust to label noise, most of the previous methods have not fully utilized data distribution information. To address this issue, this paper introduces a bi-level learning paradigm termed “Spectral Cluster Discovery'' (SCD) for combating with noisy labels. Namely, we simultaneously learn a robust classifier (Learning stage) by discovering the low-rank approximation to the ground-truth label matrix and learn an ideal affinity graph (Clustering stage). Specifically, we use the learned classifier to assign the examples with similar label to a mutual cluster. Based on the cluster membership, we utilize the learned affinity graph to explore the noisy examples based on the cluster membership. Both stages will reinforce each other iteratively. Experimental results on typical benchmark and real-world datasets verify the superiority of SCD to other label noise learning methods.
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Gui, Xian-Jin, Wei Wang, and Zhang-Hao Tian. "Towards Understanding Deep Learning from Noisy Labels with Small-Loss Criterion." In Thirtieth International Joint Conference on Artificial Intelligence {IJCAI-21}. California: International Joint Conferences on Artificial Intelligence Organization, 2021. http://dx.doi.org/10.24963/ijcai.2021/340.

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Deep neural networks need large amounts of labeled data to achieve good performance. In real-world applications, labels are usually collected from non-experts such as crowdsourcing to save cost and thus are noisy. In the past few years, deep learning methods for dealing with noisy labels have been developed, many of which are based on the small-loss criterion. However, there are few theoretical analyses to explain why these methods could learn well from noisy labels. In this paper, we theoretically explain why the widely-used small-loss criterion works. Based on the explanation, we reformalize the vanilla small-loss criterion to better tackle noisy labels. The experimental results verify our theoretical explanation and also demonstrate the effectiveness of the reformalization.
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Reports on the topic "Learning with noisy labels"

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Jin, Rong, and Anil K. Jain. Data Representation: Learning Kernels from Noisy Data and Uncertain Information. Fort Belvoir, VA: Defense Technical Information Center, July 2010. http://dx.doi.org/10.21236/ada535030.

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Multiple Engine Faults Detection Using Variational Mode Decomposition and GA-K-means. SAE International, March 2022. http://dx.doi.org/10.4271/2022-01-0616.

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As a critical power source, the diesel engine is widely used in various situations. Diesel engine failure may lead to serious property losses and even accidents. Fault detection can improve the safety of diesel engines and reduce economic loss. Surface vibration signal is often used in non-disassembly fault diagnosis because of its convenient measurement and stability. This paper proposed a novel method for engine fault detection based on vibration signals using variational mode decomposition (VMD), K-means, and genetic algorithm. The mode number of VMD dramatically affects the accuracy of extracting signal components. Therefore, a method based on spectral energy distribution is proposed to determine the parameter, and the quadratic penalty term is optimized according to SNR. The results show that the optimized VMD can adaptively extract the vibration signal components of the diesel engine. In the actual fault diagnosis case, it is difficult to obtain the data with labels. The clustering algorithm can complete the classification without labeled data, but it is limited by the low accuracy. In this paper, the optimized VMD is used to decompose and standardize the vibration signal. Then the correlation-based feature selection method is implemented to obtain the feature results after dimensionality reduction. Finally, the results are input into the classifier combined by K-means and genetic algorithm (GA). By introducing and optimizing the genetic algorithm, the number of classes can be selected automatically, and the accuracy is significantly improved. This method can carry out adaptive multiple fault detection of a diesel engine without labeled data. Compared with many supervised learning algorithms, the proposed method also has high accuracy.
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