Relevant bibliographies by topics / Convolutional Auto-Encoder

Academic literature on the topic 'Convolutional Auto-Encoder'

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Published: 6 September 2023

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Journal articles on the topic "Convolutional Auto-Encoder"

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Song, Xiaona, Haichao Liu, Lijun Wang, Song Wang, Yunyu Cao, Donglai Xu, and Shenfeng Zhang. "A Semantic Segmentation Method for Road Environment Images Based on Hybrid Convolutional Auto-Encoder." Traitement du Signal 39, no. 4 (August 31, 2022): 1235–45. http://dx.doi.org/10.18280/ts.390416.

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Deep convolutional neural networks (CNNs) have presented amazing performance in the task of semantic segmentation. However, the network model is complex, the training time is prolonged, the semantic segmentation accuracy is not high and the real-time performance is not good, so it is difficult to be directly used in the semantic segmentation of road environment images of autonomous vehicles. As one of the three models of deep learning, the auto-encoder (AE) has powerful data learning and feature extracting capabilities from the raw data itself. In this study, the network architecture of auto-encoder and convolutional auto-encoder (CAE) is improved, supervised learning auto-encoder and improved convolutional auto-encoder are proposed, and a hybrid convolutional auto-encoder model is constructed by combining them. It can extract low-dimensional abstract features of road environment images by using convolution layers and pooling layers in front of the network, and then supervised learning auto-encoder are used to enhance and express semantic segmentation features, and finally de-convolution layers and un-pooling layers are used to generate semantic segmentation results. The hybrid convolutional auto-encoder model proposed in this paper not only contains encoding and decoding parts which are used in the common semantic segmentation models, but also adds semantic feature enhancing and representing parts, so that the network which has fewer convolutional and pooling layers can still achieve better semantic segmentation effects. Compared to the semantic segmentation based on convolutional neural networks, the hybrid convolutional auto-encoder has fewer network layers, fewer network parameters, and simpler network training. We evaluated our proposed method on Camvid and Cityscapes, which are standard benchmarks for semantic segmentation, and it proved to have a better semantic segmentation effect and good real-time performance.
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Kim, Dong-Hoon, JoonWhoan Lee, and #VALUE! #VALUE! "Music Mood recognition using Convolutional Variation Auto Encoder." Journal of Korean Institute of Intelligent Systems 29, no. 5 (October 31, 2019): 352–58. http://dx.doi.org/10.5391/jkiis.2019.29.5.352.

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Theunissen, Carl Daniel, Steven Martin Bradshaw, Lidia Auret, and Tobias Muller Louw. "One-Dimensional Convolutional Auto-Encoder for Predicting Furnace Blowback Events from Multivariate Time Series Process Data—A Case Study." Minerals 11, no. 10 (October 9, 2021): 1106. http://dx.doi.org/10.3390/min11101106.

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Modern industrial mining and mineral processing applications are characterized by large volumes of historical process data. Hazardous events occurring in these processes compromise process safety and therefore overall viability. These events are recorded in historical data and are often preceded by characteristic patterns. Reconstruction-based data-driven models are trained to reconstruct the characteristic patterns of hazardous event-preceding process data with minimal residuals, facilitating effective event prediction based on reconstruction residuals. This investigation evaluated one-dimensional convolutional auto-encoders as reconstruction-based data-driven models for predicting positive pressure events in industrial furnaces. A simple furnace model was used to generate dynamic multivariate process data with simulated positive pressure events to use as a case study. A one-dimensional convolutional auto-encoder was trained as a reconstruction-based model to recognize the data preceding the hazardous events, and its performance was evaluated by comparing it to a fully-connected auto-encoder as well as a principal component analysis reconstruction model. This investigation found that one-dimensional convolutional auto-encoders recognized event-preceding patterns with lower detection delays, higher specificities, and lower missed alarm rates, suggesting that the one-dimensional convolutional auto-encoder layout is superior to the fully connected auto-encoder layout for use as a reconstruction-based event prediction model. This investigation also found that the nonlinear auto-encoder models outperformed the linear principal component model investigated. While the one-dimensional auto-encoder was evaluated comparatively on a simulated furnace case study, the methodology used in this evaluation can be applied to industrial furnaces and other mineral processing applications. Further investigation using industrial data will allow for a view of the convolutional auto-encoder’s absolute performance as a reconstruction-based hazardous event prediction model.
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Yasukawa, Shinsuke, Sreeraman Raghura, Yuya Nishida, and Kazuo Ishii. "Underwater image reconstruction using convolutional auto-encoder." Proceedings of International Conference on Artificial Life and Robotics 26 (January 21, 2021): 262–65. http://dx.doi.org/10.5954/icarob.2021.os23-4.

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Zhao, Wei, Zuchen Jia, Xiaosong Wei, and Hai Wang. "An FPGA Implementation of a Convolutional Auto-Encoder." Applied Sciences 8, no. 4 (March 27, 2018): 504. http://dx.doi.org/10.3390/app8040504.

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Li, Hongfei, Lili Meng, Jia Zhang, Yanyan Tan, Yuwei Ren, and Huaxiang Zhang. "Multiple Description Coding Based on Convolutional Auto-Encoder." IEEE Access 7 (2019): 26013–21. http://dx.doi.org/10.1109/access.2019.2900498.

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Newlin, Dev R., and C. Seldev Christopher. "De-noising of Natural Images with Better Enhancement Using Convolutional Auto-Encoder." Journal of Advanced Research in Dynamical and Control Systems 11, no. 12 (December 20, 2019): 124–36. http://dx.doi.org/10.5373/jardcs/v11i12/20193221.

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Zhu, Yi, Lei Li, and Xindong Wu. "Stacked Convolutional Sparse Auto-Encoders for Representation Learning." ACM Transactions on Knowledge Discovery from Data 15, no. 2 (April 2021): 1–21. http://dx.doi.org/10.1145/3434767.

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Deep learning seeks to achieve excellent performance for representation learning in image datasets. However, supervised deep learning models such as convolutional neural networks require a large number of labeled image data, which is intractable in applications, while unsupervised deep learning models like stacked denoising auto-encoder cannot employ label information. Meanwhile, the redundancy of image data incurs performance degradation on representation learning for aforementioned models. To address these problems, we propose a semi-supervised deep learning framework called stacked convolutional sparse auto-encoder, which can learn robust and sparse representations from image data with fewer labeled data records. More specifically, the framework is constructed by stacking layers. In each layer, higher layer feature representations are generated by features of lower layers in a convolutional way with kernels learned by a sparse auto-encoder. Meanwhile, to solve the data redundance problem, the algorithm of Reconstruction Independent Component Analysis is designed to train on patches for sphering the input data. The label information is encoded using a Softmax Regression model for semi-supervised learning. With this framework, higher level representations are learned by layers mapping from image data. It can boost the performance of the base subsequent classifiers such as support vector machines. Extensive experiments demonstrate the superior classification performance of our framework compared to several state-of-the-art representation learning methods.
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Zhou, Jian, Xianwei Wei, Chunling Cheng, Qidong Yang, and Qun Li. "Multimodal Emotion Recognition Method Based on Convolutional Auto-Encoder." International Journal of Computational Intelligence Systems 12, no. 1 (2019): 351. http://dx.doi.org/10.2991/ijcis.2019.125905651.

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Oh, Junghyun, and Beomhee Lee. "Condition-invariant Place Recognition Using Deep Convolutional Auto-encoder." Journal of Korea Robotics Society 14, no. 1 (March 30, 2019): 8–13. http://dx.doi.org/10.7746/jkros.2019.14.1.008.

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Dissertations / Theses on the topic "Convolutional Auto-Encoder"

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Ionascu, Beatrice. "Modelling user interaction at scale with deep generative methods." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-239333.

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Understanding how users interact with a company's service is essential for data-driven businesses that want to better cater to their users and improve their offering. By using a generative machine learning approach it is possible to model user behaviour and generate new data to simulate or recognize and explain typical usage patterns. In this work we introduce an approach for modelling users' interaction behaviour at scale in a client-service model. We propose a novel representation of multivariate time-series data as time pictures that express temporal correlations through spatial organization. This representation shares two key properties that convolutional networks have been built to exploit and allows us to develop an approach based on deep generative models that use convolutional networks as backbone. In introducing this approach of feature learning for time-series data, we expand the application of convolutional neural networks in the multivariate time-series domain, and specifically user interaction data. We adopt a variational approach inspired by the β-VAE framework in order to learn hidden factors that define different user behaviour patterns. We explore different values for the regularization parameter β and show that it is possible to construct a model that learns a latent representation of identifiable and different user behaviours. We show on real-world data that the model generates realistic samples, that capture the true population-level statistics of the interaction behaviour data, learns different user behaviours, and provides accurate imputations of missing data.
Förståelse för hur användare interagerar med ett företags tjänst är essentiell för data-drivna affärsverksamheter med ambitioner om att bättre tillgodose dess användare och att förbättra deras utbud. Generativ maskininlärning möjliggör modellering av användarbeteende och genererande av ny data i syfte att simulera eller identifiera och förklara typiska användarmönster. I detta arbete introducerar vi ett tillvägagångssätt för storskalig modellering av användarinteraktion i en klientservice-modell. Vi föreslår en ny representation av multivariat tidsseriedata i form av tidsbilder vilka representerar temporala korrelationer via spatial organisering. Denna representation delar två nyckelegenskaper som faltningsnätverk har utvecklats för att exploatera, vilket tillåter oss att utveckla ett tillvägagångssätt baserat på på djupa generativa modeller som bygger på faltningsnätverk. Genom att introducera detta tillvägagångssätt för tidsseriedata expanderar vi applicering av faltningsnätverk inom domänen för multivariat tidsserie, specifikt för användarinteraktionsdata. Vi använder ett tillvägagångssätt inspirerat av ramverket β-VAE i syfte att lära modellen gömda faktorer som definierar olika användarmönster. Vi utforskar olika värden för regulariseringsparametern β och visar att det är möjligt att konstruera en modell som lär sig en latent representation av identifierbara och multipla användarbeteenden. Vi visar med verklig data att modellen genererar realistiska exempel vilka i sin tur fångar statistiken på populationsnivå hos användarinteraktionsdatan, samt lär olika användarbeteenden och bidrar med precisa imputationer av saknad data.
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Sayadi, Karim. "Classification du texte numérique et numérisé. Approche fondée sur les algorithmes d'apprentissage automatique." Thesis, Paris 6, 2017. http://www.theses.fr/2017PA066079/document.

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Différentes disciplines des sciences humaines telles la philologie ou la paléographie font face à des tâches complexes et fastidieuses pour l'examen des sources de données. La proposition d'approches computationnelles en humanités permet d'adresser les problématiques rencontrées telles que la lecture, l'analyse et l'archivage de façon systématique. Les modèles conceptuels élaborés reposent sur des algorithmes et ces derniers donnent lieu à des implémentations informatiques qui automatisent ces tâches fastidieuses. La première partie de la thèse vise, d'une part, à établir la structuration thématique d'un corpus, en construisant des espaces sémantiques de grande dimension. D'autre part, elle vise au suivi dynamique des thématiques qui constitue un réel défi scientifique, notamment en raison du passage à l'échelle. La seconde partie de la thèse traite de manière holistique la page d'un document numérisé sans aucune intervention préalable. Le but est d'apprendre automatiquement des représentations du trait de l'écriture ou du tracé d'un certain script par rapport au tracé d'un autre script. Il faut dans ce cadre tenir compte de l'environnement où se trouve le tracé : image, artefact, bruits dus à la détérioration de la qualité du papier, etc. Notre approche propose un empilement de réseaux de neurones auto-encodeurs afin de fournir une représentation alternative des données reçues en entrée
Different disciplines in the humanities, such as philology or palaeography, face complex and time-consuming tasks whenever it comes to examining the data sources. The introduction of computational approaches in humanities makes it possible to address issues such as semantic analysis and systematic archiving. The conceptual models developed are based on algorithms that are later hard coded in order to automate these tedious tasks. In the first part of the thesis we propose a novel method to build a semantic space based on topics modeling. In the second part and in order to classify historical documents according to their script. We propose a novel representation learning method based on stacking convolutional auto-encoder. The goal is to automatically learn plot representations of the script or the written language
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Chen, Peng-Cheng, and 陳鵬丞. "Semi-Supervised Learning Framework with an Auto-Elastic Convolutional Auto-Encoder for Image Classification Design." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/sv6v8s.

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碩士
逢甲大學
自動控制工程學系
106
More and more attention of the deep learning for image and speech recognition, even artificial intelligence (AI) beats humans in ancient game of Go has been attracted with the development of computer and electronic devices. In neural networks, deep learning with convolutional neural network (CNN) has been widely utilized in numerous applications of automatic image recognition, such as multiple object detection and classification, object tracking, sematic segmentation, and most of them have achieved outstanding results. However, with the applications are becoming more complex and various deep learning frameworks are constantly evolving, the neural networks need multiple layers in order to learn more detailed and more abstractions relationships within the data, and then it can enhance the efficiency of the recognition. Some popular types of deep neural networks often requires lots of labeled data as well as huge computing power and for this reason they are not easily to be integrated into embedded system. Indeed, sometimes problems don't need more layer neural networks to solve it, the relatively simple environment may have achieved better results using first few layers merely. Thus, it causes that the excess memory goes to waste and tedious and time-consuming process. Hence, the thesis presents a novel semi-supervised learning framework with an auto-elastic convolutional auto-encoder for image detection and classification. The proposed an auto-elastic convolutional auto-encoder learning framework is based on the Actor-Critic reinforcement learning approach. The Actor-Critic is the learning of a mapping from situations to actions, which learns from the consequences of its actions, rather than from being unequivocally instructed and it selects its actions on basis of its past experiences and also by new choices. It is a trial and error learning essentially. The main contribution of this study is to repeatedly generate the convolution kernels based on Actor-Critic algorithms and then determine the optimal number of layers of neural network framework so that the whole neural network framework can achieve self-elasticity function depending on task complexity. The novel Auto-Elastic Convolutional Auto-Encoder framework is mainly divided into four parts: auto-elastic unit, data generation unit, memory unit, learner unit. First, the auto-elastic unit is based on the proposed Actor-Critic encoder (ACE) algorithms. The Actor generates a coding policy (action) by using current image/feature map and historical convolution kernels information. The Critic as a value estimator, whereas the actor attempts to select actions (coding policy) based on the value function estimated by the critic. Next, memory unit is used to store the coding results and historical information from the auto-elastic unit, such as corresponding rewards, historical convolution kernels, feature maps, etc., and combine a data generation unit to generate training sample data automatically. Final, the learner unit is proposed as for the proposed semi-supervised learning framework training. Extensively simulation, quantitative analysis and comparison results demonstrate the feasibility and efficiency of the proposed auto-elastic convolutional auto-encoder learning framework for digit recognition and classification.
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Liao, Chi-Jou, and 廖綺柔. "Using Convolutional Neural Network Auto-encoder in Breast Tumors Classification and Detection Compare with Traditional Ultrasound BIRADS." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/tfz345.

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Paul, Subir. "Hyperspectral Remote Sensing for Land Cover Classification and Chlorophyll Content Estimation using Advanced Machine Learning Techniques." Thesis, 2020. https://etd.iisc.ac.in/handle/2005/4537.

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In the recent years, remote sensing data or images have great potential for continuous spatial and temporal monitoring of Earth surface features. In case of optical remote sensing, hyperspectral (HS) data contains abundant spectral information and these information are advantageous for various applications. However, high-dimensional HS data handling is a very challenging task. Different techniques are proposed as a part of this thesis to handle the HS data in a computationally efficient manner and to achieve better performance for land cover classification and chlorophyll content prediction. Prior to start the HS data application, multispectral (MS) data are also analyzed in this thesis for crop classification. Multi-temporal MS data is used for crop classification. Landsat-8 operational land imager (OLI) sensor data are considered as MS data in this work. Surface reflectances and derived normalized difference indices (NDIs) of multi-temporal MS bands are combinedly used for the crop classification. Different dimensionality reduction techniques, viz. feature selection (FS) (e.g. random forest (RF) and partial informational correlation (PIC) measure-based), linear (e.g. principal component analysis (PCA) and independent component analysis) and nonlinear feature extraction (FE) (e.g. kernel PCA and Autoencoder), to be employed on the multi-temporal surface reflectances and NDIs datasets, are evaluated to detect the most favorable features. Subsequently, the detected features are used in a promising nonparametric classifier, support vector machine (SVM), for crop classification. It is found that all the evaluated FE techniques, employed on the multi-temporal datasets, resulted in better performance compared to FS-based approaches. PCA, being a simple and efficient FE algorithm, is well-suited in crop classification in terms of computational complexity and classification performances. Multi-temporal images are proved to be more advantageous compared to the single-date imagery for crop identification. HS data comprises of continuous spectral responses of hundreds of narrow spectral bands with very fine spectral resolution or bandwidth, which offer feature identification and classification with high accuracy. HS data are enriched with highly resourceful abundant spectral bands compared to only 5-10 spectral bands of MS data. However, analyzing and interpreting these ample amounts of data is a challenging task. Optimal spectral bands or features should be chosen or extracted to address the issue of redundancy and to capitalize on the absolute advantages of HS data. FS and FE are two broad categories of dimensionality reduction techniques. In this thesis, a FS and a FE-based computationally efficient dimensionality reduction technique is proposed for land cover classification. PIC-based HS band selection approach is proposed as a FS-based dimensionality reduction technique for classification of land cover types. PIC measure is more skillful compared to mutual information for estimation of non-parametric conditional dependency. In this proposed approach, HS narrow-bands are selected in an innovative way utilizing the PIC. Firstly, HS bands are divided into different spectral groups or segments using normalized mutual information (NMI) and then PIC is employed to each spectral group for optimal band selection. This approach is more efficient in terms of computational time and in generalizing the applicability of selected spectral bands. Further, these optimal spectral bands are used in the SVM and RF classifier for classification of land cover types and performance evaluation. The proposed FS-based dimensionality reduction approach is compared with different state-of-the-art techniques for land cover classification. The proposed methodology improved the classification performances compared to the existing techniques and the advancement in performances are proven to be statistically significant. In the recent years, deep learning-based FE techniques are very popular and also proven to be effective in extraction of apt features from the high-dimensional data. However, these techniques are computationally expensive. A computationally efficient FE-based dimensionality reduction approach, NMI-based segmented stacked auto-encoder (S-SAE), is proposed for extraction of spectral features from the HS data. These spectral features are consecutively utilized for creation of spatial features and later both spectral and spatial features are used in the classifier models (i.e. SVM and RF) for land cover classification. The proposed HS image classification approach reduces the complexity and computational time compared to the available techniques. A non-parametric dependency measure (i.e. NMI) based spectral segmentation is proposed instead of linear and parametric dependency measure to take care of the both linear and nonlinear inter-band dependencies for spectral segmentation of the HS bands. Then extended morphological profiles (EMPs) are created corresponding to segmented spectral features to assimilate the spatial information in the spectral-spatial classification approach. Two non-parametric classifiers, SVM with Gaussian kernel and RF are used for classification of the three most popularly used HS datasets. The experiments performed with the proposed methodology provide encouraging results compared to numerous existing approaches. HS data are proven to be more resourceful compared to MS data for object detection, classification and several other applications. However, absence of any space-borne HS sensor and high cost and limited obtainability of airborne sensors-based images limit the use of HS data. Transformation of readily available MS data into quasi-HS data can be a feasible solution for this issue. A deep learning-based regression algorithm, convolutional neural network regression (CNNR), is proposed as part of this thesis for MS (i.e. Landsat-7/8) to quasi-HS (i.e. quasi-Hyperion) data transformation. CNNR model introduces the advantages of nonlinear modelling and assimilation of spatial information in the regression-based modelling. The proposed CNNR model is compared with the pseudo-HS image transformation algorithm (PHITA), stepwise linear regression (SLR), and support vector regression (SVR) models by evaluating the quality of the quasi-Hyperion data. Several statistical metrics are calculated to compare each band’s reflectance values as well as spectral reflectance curve of each pixel of the quasi-Hyperion data with that of the original Hyperion data. The developed models and generated quasi-Hyperion data are also evaluated with application to crop classification. Analyzing the results of all the experiments, it is evident that CNNR model is more efficient compared to PHITA, SLR, and SVR in creating the quasi-Hyperion data and this transformed data are proven to be resourceful for crop classification application. The proposed CNNR model-based MS to quasi-HS data transformation approach can be used as a viable alternative for different applications in the absence of original HS images. HS data are investigated for estimation of chlorophyll content, which is one of the essential biochemical parameters to assess the growth process of the fruit trees. This study developed a model for estimation of canopy averaged chlorophyll content (CACC) of pear trees using the convolutional auto-encoder (CAE) features of HS data. This study also demonstrated the inspection of anomaly among the trees by employing multi-dimensional scaling (MDS) on the CAE features and detected the outlier trees, prior to fit nonlinear regression models. These outlier trees are excluded from further experiments which helped in improving the prediction performance of CACC. Gaussian process regression (GPR) and support vector regression (SVR) techniques are investigated as nonlinear regression models and used for prediction of CACC. The CAE features are proven to be providing better prediction of CACC, compared to the direct use of HS bands or vegetation indices as predictors. Training of the regression models, excluding the outlier trees, improved the CACC prediction performance. It is evident from the experiments that GPR can predict the CACC with better accuracy compared to SVR. In addition, the reliability of the tree canopy masks, which are utilized for averaging the features’ values for a particular tree, is also evaluated.
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Book chapters on the topic "Convolutional Auto-Encoder"

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Chen, Wei, Ruimin Hu, Xiaochen Wang, and Dengshi Li. "HRTF Representation with Convolutional Auto-encoder." In MultiMedia Modeling, 605–16. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-37731-1_49.

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Wang, Taizheng, Chunyang Ye, Hui Zhou, Mingwang Ou, and Bo Cheng. "AIS Ship Trajectory Clustering Based on Convolutional Auto-encoder." In Advances in Intelligent Systems and Computing, 529–46. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-55187-2_39.

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Wang, Diangang, Wei Gan, Chenyang Yan, Kun Huang, and Hongyi Wu. "Inception Model of Convolutional Auto-encoder for Image Denoising." In Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, 174–86. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-64214-3_12.

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Shamsi, Meysam, Damien Lolive, Nelly Barbot, and Jonathan Chevelu. "Script Selection Using Convolutional Auto-encoder for TTS Speech Corpus." In Speech and Computer, 423–32. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-26061-3_43.

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Xu, Chaoyang, Ling Wu, and Shiping Wang. "Unsupervised Dimension Reduction for Image Classification Using Regularized Convolutional Auto-Encoder." In Advances in Intelligent Systems and Computing, 99–108. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-17795-9_8.

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Han, Xiaobing, Yanfei Zhong, Lifang He, Philip S. Yu, and Liangpei Zhang. "The Unsupervised Hierarchical Convolutional Sparse Auto-Encoder for Neuroimaging Data Classification." In Brain Informatics and Health, 156–66. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-23344-4_16.

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Arslan, Abdullah Taha, and Ugur Yayan. "Convolutional Auto-Encoder Based Degradation Point Forecasting for Bearing Data Set." In Artificial Intelligence and Applied Mathematics in Engineering Problems, 817–29. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-36178-5_71.

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Xiang, Xinyu, Ping Zhang, Qiang Yuan, Renping Li, Runqiao Hu, and Ke Li. "Few-Shot Learning Based on Convolutional Denoising Auto-encoder Relational Network." In Communications in Computer and Information Science, 103–12. Singapore: Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-9247-5_8.

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Li, Chun, Wenfeng Shi, and Lin Shang. "Latent Feature Representation for Cohesive Community Detection Based on Convolutional Auto-Encoder." In Big Data, 380–94. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-15-1899-7_27.

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Shuvo, M. I. R., M. A. H. Akhand, and N. Siddique. "Handwritten Numeral Superposition to Printed Form Using Convolutional Auto-Encoder and Recognition Using Convolutional Neural Network." In Proceedings of International Joint Conference on Computational Intelligence, 179–90. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-3607-6_14.

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Conference papers on the topic "Convolutional Auto-Encoder"

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Ye, Hao, Le Liang, and Geoffrey Ye Li. "Circular Convolutional Auto-Encoder for Channel Coding." In 2019 IEEE 20th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC). IEEE, 2019. http://dx.doi.org/10.1109/spawc.2019.8815483.

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Sharma, Manish, Panos P. Markopoulos, Eli Saber, M. Salman Asif, and Ashley Prater-Bennette. "Convolutional Auto-Encoder with Tensor-Train Factorization." In 2021 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW). IEEE, 2021. http://dx.doi.org/10.1109/iccvw54120.2021.00027.

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Wu, Hao, Ziyang Zheng, Yong Li, Wenrui Dai, and Hongkai Xiong. "Compressed Sensing via a Deep Convolutional Auto-encoder." In 2018 IEEE Visual Communications and Image Processing (VCIP). IEEE, 2018. http://dx.doi.org/10.1109/vcip.2018.8698640.

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Alqahtani, A., X. Xie, J. Deng, and M. W. Jones. "A Deep Convolutional Auto-Encoder with Embedded Clustering." In 2018 25th IEEE International Conference on Image Processing (ICIP). IEEE, 2018. http://dx.doi.org/10.1109/icip.2018.8451506.

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Turchenko, Volodymyr, and Artur Luczak. "Creation of a deep convolutional auto-encoder in Caffe." In 2017 9th IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications (IDAACS). IEEE, 2017. http://dx.doi.org/10.1109/idaacs.2017.8095172.

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Baccouche, Moez, Franck Mamalet, Christian Wolf, Christophe Garcia, and Atilla Baskurt. "Spatio-Temporal Convolutional Sparse Auto-Encoder for Sequence Classification." In British Machine Vision Conference 2012. British Machine Vision Association, 2012. http://dx.doi.org/10.5244/c.26.124.

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Schuch, Patrick, Simon Schulz, and Christoph Busch. "De-convolutional auto-encoder for enhancement of fingerprint samples." In 2016 Sixth International Conference on Image Processing Theory, Tools and Applications (IPTA). IEEE, 2016. http://dx.doi.org/10.1109/ipta.2016.7821036.

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Zuo, Haolan. "CDAE-C: A Fully Convolutional Denoising Auto-Encoder with 2.5D Convolutional Classifier." In 2022 IEEE Conference on Telecommunications, Optics and Computer Science (TOCS). IEEE, 2022. http://dx.doi.org/10.1109/tocs56154.2022.10015922.

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Geng, Chi, and JianXin Song. "Human Action Recognition based on Convolutional Neural Networks with a Convolutional Auto-Encoder." In 2015 5th International Conference on Computer Sciences and Automation Engineering (ICCSAE 2015). Paris, France: Atlantis Press, 2016. http://dx.doi.org/10.2991/iccsae-15.2016.173.

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Tian, Sirui, Chao Wang, and Hong Zhang. "SAR Object Classification with a Multi-Scale Convolutional Auto-Encoder." In 2019 SAR in Big Data Era (BIGSARDATA). IEEE, 2019. http://dx.doi.org/10.1109/bigsardata.2019.8858491.

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