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Elsevier DOI
1909
VAE, Image caption, Generative models, Semi-supervised
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DAEN: Deep Autoencoder Networks for Hyperspectral Unmixing,
GeoRS(57), No. 7, July 2019, pp. 4309-4321.
IEEE DOI
1907
Hyperspectral imaging, Estimation, Computers, Training,
Noise reduction, Deep autoencoder network (DAEN), deep learning,
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Pesteie, M.,
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Adaptive Augmentation of Medical Data Using Independently Conditional
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MedImg(38), No. 12, December 2019, pp. 2807-2820.
IEEE DOI
1912
Training, Data models, Biomedical imaging, Adaptation models,
Image segmentation, Feature extraction, Computational modeling,
tumor segmentation
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Jiang, S.[Shuoran],
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Mixture variational autoencoders,
PRL(128), 2019, pp. 263-269.
Elsevier DOI
1912
Mixture variational autoencoders, Mixture models,
Reparameterization trick, SGVB estimator
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Kossyk, I.[Ingo],
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Discriminative regularization of the latent manifold of variational
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Elsevier DOI
1906
Variational auto-encoder, Regularization,
Knowledge representation, Perceptual data compaction,
Statistical performance analysis
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Liu, S.[Shiqi],
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2005
Variational autoencoder, Mutual information, Generative model
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IEEE DOI
2002
BibRef
Joo, W.Y.[Weon-Young],
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Dirichlet Variational Autoencoder,
PR(107), 2020, pp. 107514.
Elsevier DOI
2008
Representation learning, Variational autoencoder,
Deep generative model, Multi-modal latent representation, Component collapse
BibRef
Xie, W.,
Yang, J.,
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SRUN: Spectral Regularized Unsupervised Networks for Hyperspectral
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GeoRS(58), No. 2, February 2020, pp. 1463-1474.
IEEE DOI
2001
Feature extraction, Object detection, Hyperspectral imaging,
Anomaly detection, Decoding, Aircraft, Background suppression,
variational autoencoders (VAEs)
BibRef
Nazábal, A.[Alfredo],
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Handling incomplete heterogeneous data using VAEs,
PR(107), 2020, pp. 107501.
Elsevier DOI
2008
Generative models, Variational autoencoders, Incomplete heterogenous data
BibRef
Gao, R.,
Hou, X.,
Qin, J.,
Chen, J.,
Liu, L.,
Zhu, F.,
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Zero-VAE-GAN: Generating Unseen Features for Generalized and
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IP(29), 2020, pp. 3665-3680.
IEEE DOI
2002
Zero-shot learning, generative model, self-training
BibRef
Shao, J.,
Li, X.,
Generalized Zero-Shot Learning With Multi-Channel Gaussian Mixture
VAE,
SPLetters(27), 2020, pp. 456-460.
IEEE DOI
2004
Visualization, Semantics, Training, Feature extraction,
Gaussian distribution, Cats, Benchmark testing, gaussian mixture VAE
BibRef
Wang, X.,
Tan, K.,
Du, Q.,
Chen, Y.,
Du, P.,
CVA2E: A Conditional Variational Autoencoder With an Adversarial
Training Process for Hyperspectral Imagery Classification,
GeoRS(58), No. 8, August 2020, pp. 5676-5692.
IEEE DOI
2007
Generative adversarial networks, Training,
Hyperspectral imaging, Data models, Generators,
variational autoencoder (VAE)
BibRef
Lu, G.Q.[Guang-Quan],
Zhao, X.S.[Xi-Shun],
Yin, J.[Jian],
Yang, W.W.[Wei-Wei],
Li, B.[Bo],
Multi-task learning using variational auto-encoder for sentiment
classification,
PRL(132), 2020, pp. 115-122.
Elsevier DOI
2005
Sentiment classification, Opinion mining, Deep learning,
Multi-task learning, Variational auto-encoder, LSTM, Big data
BibRef
Gordon, J.[Jonathan],
Hernández-Lobato, J.M.[José Miguel],
Combining deep generative and discriminative models for Bayesian
semi-supervised learning,
PR(100), 2020, pp. 107156.
Elsevier DOI
2005
Probabilistic models, Semi-supervised learning,
Variational autoencoders, Predictive uncertainty
BibRef
Patacchiola, M.[Massimiliano],
Fox-Roberts, P.[Patrick],
Rosten, E.[Edward],
Y-Autoencoders: Disentangling latent representations via sequential
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Elsevier DOI
2012
Disentangled representations, Deep learning, Autoencoders, Generative models
BibRef
Milani, S.[Simone],
A distributed source autoencoder of local visual descriptors for 3D
reconstruction,
PRL(146), 2021, pp. 193-199.
Elsevier DOI
2105
Distributed vision networks, Distributed source coding,
Autoencoder, Local descriptor coding, Structure-from-Motion
BibRef
Kamikawa, Y.[Yuta],
Hashimoto, A.[Atsushi],
Sonogashira, M.[Motoharu],
Iiyama, M.[Masaaki],
Curiosity Guided Fine-Tuning for Encoder-Decoder-Based Visual
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IEICE(E104-D), No. 5, May 2021, pp. 752-761.
WWW Link.
2105
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Takahashi, R.[Ryuhei],
Hashimoto, A.[Atsushi],
Sonogashira, M.[Motoharu],
Iiyama, M.[Masaaki],
Partially-shared Variational Auto-encoders for Unsupervised Domain
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ECCV20(XVI: 1-17).
Springer DOI
2010
BibRef
Bansal, V.[Vipul],
Buckchash, H.[Himanshu],
Raman, B.[Balasubramanian],
Discriminative Auto-Encoding for Classification and Representation
Learning Problems,
SPLetters(28), 2021, pp. 987-991.
IEEE DOI
2106
Training, Task analysis, Decoding, Mathematical model,
Signal to noise ratio, Image reconstruction, Estimation,
Representation learning
BibRef
Jin, K.H.[Kyong Hwan],
Deep Block Transform for Autoencoders,
SPLetters(28), 2021, pp. 1016-1019.
IEEE DOI
2106
Convolution, Transforms, Kernel, Training,
Discrete cosine transforms, Convolutional neural networks,
convolutional neural network
BibRef
Huang, X.[Xiang],
Gai, S.[Shan],
Reduced Biquaternion Stacked Denoising Convolutional AutoEncoder for
RGB-D Image Classification,
SPLetters(28), 2021, pp. 1205-1209.
IEEE DOI
2106
Training, Convolution, Feature extraction, Tensors, Noise reduction,
Matrix converters, Image color analysis, Reduced biquaternion,
hypercomplex network
BibRef
Abrol, V.[Vinayak],
Sharma, P.[Pulkit],
Patra, A.[Arijit],
Improving Generative Modelling in VAEs Using Multimodal Prior,
MultMed(23), 2021, pp. 2153-2161.
IEEE DOI
2107
Object oriented modeling, Training, Mutual information, Decoding,
Kernel, Data models, Uncertainty, Generative modelling, autoencoders,
representation learning
BibRef
Chen, Z.[Zhitao],
Tong, L.[Lei],
Qian, B.[Bin],
Yu, J.[Jing],
Xiao, C.B.[Chuang-Bai],
Self-Attention-Based Conditional Variational Auto-Encoder Generative
Adversarial Networks for Hyperspectral Classification,
RS(13), No. 16, 2021, pp. xx-yy.
DOI Link
2109
BibRef
Hou, Y.Z.[Ying-Zhen],
Zhai, J.H.[Jun-Hai],
Chen, J.K.[Jian-Kai],
Coupled adversarial variational autoencoder,
SP:IC(98), 2021, pp. 116396.
Elsevier DOI
2109
Image pairs, Adversarial variational autoencoder, Resolution,
Adversarial learning, Attribute transformation
BibRef
Pan, J.[Jing],
Qian, Y.H.[Yu-Hua],
Li, F.[Feijiang],
Guo, Q.[Qian],
Image deep clustering based on local-topology embedding,
PRL(151), 2021, pp. 88-94.
Elsevier DOI
2110
Deep clustering, Autoencoder, Local-topology embedding,
Data augmentation, Unsupervised learning
BibRef
Albarracín, J.F.H.[Juan F. Hernández],
Rivera, A.R.[Adín Ramírez],
Video Reenactment as Inductive Bias for Content-Motion
Disentanglement,
IP(31), 2022, pp. 2365-2374.
IEEE DOI
2203
Task analysis, Image reconstruction, Representation learning,
Random access memory, Decoding, Data models, Random variables,
self-supervised learning
BibRef
Kamal, I.M.[Imam Mustafa],
Bae, H.[Hyerim],
Super-encoder with cooperative autoencoder networks,
PR(126), 2022, pp. 108562.
Elsevier DOI
2204
Autoencoder, Dimensionality reduction, Feature extraction,
Pattern recognition, Cooperative neural networks
BibRef
Yu, J.C.[Jun-Chi],
Xu, T.[Tingyang],
Rong, Y.[Yu],
Huang, J.Z.[Jun-Zhou],
He, R.[Ran],
Structure-aware conditional variational auto-encoder for constrained
molecule optimization,
PR(126), 2022, pp. 108581.
Elsevier DOI
2204
Molecule optimization, Conditional generation, Drug discovery
BibRef
Chang, J.H.[Jian-Hui],
Zhao, Z.H.[Zheng-Hui],
Jia, C.M.[Chuan-Min],
Wang, S.Q.[Shi-Qi],
Yang, L.B.[Ling-Bo],
Mao, Q.[Qi],
Zhang, J.[Jian],
Ma, S.W.[Si-Wei],
Conceptual Compression via Deep Structure and Texture Synthesis,
IP(31), No. 2022, pp. 2809-2823.
IEEE DOI
2204
Image coding, Visualization, Task analysis, Image reconstruction,
Image edge detection, Transform coding, Decoding,
structure and texture
BibRef
Chang, J.H.[Jian-Hui],
Mao, Q.[Qi],
Zhao, Z.H.[Zheng-Hui],
Wang, S.S.[Shan-She],
Wang, S.Q.[Shi-Qi],
Zhu, H.[Hong],
Ma, S.W.[Si-Wei],
Layered Conceptual Image Compression Via Deep Semantic Synthesis,
ICIP19(694-698)
IEEE DOI
1910
Integrating the advantages of both variational auto-encoders (VAEs)
and generative adversarial networks (GANs).
Image compression, generative models, low bitrate coding
BibRef
Zhu, J.P.[Jia-Peng],
Zhao, D.L.[De-Li],
Zhang, B.[Bo],
Zhou, B.L.[Bo-Lei],
Disentangled Inference for GANs With Latently Invertible Autoencoder,
IJCV(130), No. 5, May 2022, pp. 1259-1276.
Springer DOI
2205
BibRef
Yamaguchi, K.[Kota],
CanvasVAE: Learning to Generate Vector Graphic Documents,
ICCV21(5461-5469)
IEEE DOI
2203
Graphics, Geometry, Visualization, Image resolution, Shape,
Computational modeling, Neural generative models,
Representation learning
BibRef
Hahner, S.[Sara],
Garcke, J.[Jochen],
Mesh Convolutional Autoencoder for Semi-Regular Meshes of Different
Sizes,
WACV22(2344-2353)
IEEE DOI
2202
Geometry, Visualization, Shape, Transforms, Semi- and Un- supervised Learning
BibRef
Sharma, R.[Renuka],
Mashkaria, S.[Satvik],
Awate, S.P.[Suyash P.],
A Semi-supervised Generalized VAE Framework for Abnormality Detection
using One-Class Classification,
WACV22(1302-1310)
IEEE DOI
2202
Training, Support vector machines, Deep learning,
Shape, Neural networks, Training data,
Medical Imaging/Imaging for Bioinformatics/Biological and Cell Microscopy
BibRef
Vercheval, N.[Nicolas],
Piurica, A.[Aleksandra],
Hierarchical Variational Autoencoders for Visual Counterfactuals,
ICIP21(2513-2517)
IEEE DOI
2201
Visualization, Image processing, Semantics, Tools,
Artificial intelligence, Stress, Counterfactuals
BibRef
Inoue, N.[Nakamasa],
Yamada, R.[Ryota],
Kawakami, R.[Rei],
Sato, I.[Ikuro],
Disentangling Latent Groups of Factors,
ICIP21(2548-2552)
IEEE DOI
2201
Training, Image processing, Image retrieval, Prediction algorithms,
Decoding, Task analysis, Variational autoencoders,
Unsupervised contrastive learning
BibRef
Nakagawa, N.[Nao],
Togo, R.[Ren],
Ogawa, T.[Takahiro],
Haseyama, M.[Miki],
Interpretable Representation Learning on Natural Image Datasets via
Reconstruction in Visual-Semantic Embedding Space,
ICIP21(2473-2477)
IEEE DOI
2201
Semantics, Task analysis, Image reconstruction,
Unsupervised learning, Disentanglement, representation learning,
vision and language
BibRef
Sadeghi, M.[Mohammadreza],
Armanfard, N.[Narges],
IDECF: Improved Deep Embedding Clustering With Deep Fuzzy Supervision,
ICIP21(1009-1013)
IEEE DOI
2201
Deep learning, Clustering algorithms, Benchmark testing,
Task analysis, Image reconstruction, deep clustering,
fuzzy supervision
BibRef
Keller, T.A.[T. Anderson],
Welling, M.[Max],
Predictive Coding with Topographic Variational Autoencoders,
VIPriors21(1086-1091)
IEEE DOI
2112
Visualization,
Computational modeling, Predictive models, Predictive coding, Brain modeling
BibRef
Sharma, M.[Manish],
Markopoulos, P.P.[Panos P.],
Saber, E.[Eli],
Asif, M.S.[M. Salman],
Prater-Bennette, A.[Ashley],
Convolutional Auto-Encoder with Tensor-Train Factorization,
RSLCV21(198-206)
IEEE DOI
2112
Convolution, Training data, Machine learning,
Network architecture, Feature extraction, Task analysis
BibRef
Yang, M.Y.[Meng-Yue],
Liu, F.[Furui],
Chen, Z.T.[Zhi-Tang],
Shen, X.W.[Xin-Wei],
Hao, J.Y.[Jian-Ye],
Wang, J.[Jun],
CausalVAE:
Disentangled Representation Learning via Neural Structural Causal Models,
CVPR21(9588-9597)
IEEE DOI
2111
Analytical models, Directed acyclic graph,
Semantics, Transforms, Benchmark testing, Data models
BibRef
Kim, J.[Jinwoo],
Yoo, J.[Jaehoon],
Lee, J.H.[Ju-Ho],
Hong, S.[Seunghoon],
SetVAE: Learning Hierarchical Composition for Generative Modeling of
Set-Structured Data,
CVPR21(15054-15063)
IEEE DOI
2111
Art, Data models, Cognition, Encoding,
Pattern recognition, Task analysis
BibRef
Zhu, X.Q.[Xin-Qi],
Xu, C.[Chang],
Tao, D.C.[Da-Cheng],
Where and What? Examining Interpretable Disentangled Representations,
CVPR21(5857-5866)
IEEE DOI
2111
Codes, Image coding, Perturbation methods,
Computational modeling, Encoding, Pattern recognition
BibRef
Park, J.[Jiwoong],
Cho, J.[Junho],
Chang, H.J.[Hyung Jin],
Choi, J.Y.[Jin Young],
Unsupervised Hyperbolic Representation Learning via Message Passing
Auto-Encoders,
CVPR21(5512-5522)
IEEE DOI
2111
Geometry, Visualization, Codes, Message passing,
Supervised learning, Space exploration
BibRef
Parmar, G.[Gaurav],
Li, D.C.[Da-Cheng],
Lee, K.[Kwonjoon],
Tu, Z.W.[Zhuo-Wen],
Dual Contradistinctive Generative Autoencoder,
CVPR21(823-832)
IEEE DOI
2111
Interpolation, Image resolution, Image synthesis,
Computational modeling, Generative adversarial networks, Encoding
BibRef
Daniel, T.[Tal],
Tamar, A.[Aviv],
Soft-IntroVAE: Analyzing and Improving the Introspective Variational
Autoencoder,
CVPR21(4389-4398)
IEEE DOI
2111
Training, Codes, Image synthesis,
Computational modeling, Stability analysis, Entropy
BibRef
Braunsmann, J.[Juliane],
Rajkovic, M.[Marko],
Rumpf, M.[Martin],
Wirth, B.[Benedikt],
Learning low bending and low distortion manifold embeddings,
Diff-CVML21(4411-4419)
IEEE DOI
2109
Manifolds, Interpolation, Monte Carlo methods,
Training data, Transforms, Machine learning
BibRef
Lee, M.[Mihee],
Pavlovic, V.[Vladimir],
Private-Shared Disentangled Multimodal VAE for Learning of Latent
Representations,
MULA21(1692-1700)
IEEE DOI
2109
Computational modeling, Data models, Pattern recognition,
Internet, Task analysis
BibRef
Rodríguez, E.G.[Elliott Gordon],
On Disentanglement and Mutual Information in Semi-Supervised
Variational Auto-Encoders,
LXCV21(1257-1262)
IEEE DOI
2109
Pattern recognition, Mutual information
BibRef
Goto, K.[Keita],
Inoue, N.[Nakamasa],
Learning VAE with Categorical Labels for Generating Conditional
Handwritten Characters,
MVA21(1-5)
DOI Link
2109
Image synthesis, Semantics, Data models, Task analysis
BibRef
He, Z.[Zhixun],
Singhal, M.[Mukesh],
Adversarial Defense Through High Frequency Loss Variational
Autoencoder Decoder and Bayesian Update With Collective Voting,
MVA21(1-7)
DOI Link
2109
Deep learning, Perturbation methods,
Bayes methods, Decoding, High frequency
BibRef
Ojeda, C.[César],
Sánchez, R.J.[Ramsés J.],
Cvejoski, K.[Kostadin],
Schücker, J.[Jannis],
Bauckhagez, C.[Christian],
Georgievz, B.[Bogdan],
Auto Encoding Explanatory Examples with Stochastic Paths,
ICPR21(6219-6226)
IEEE DOI
2105
Interpolation, Semantics, Decision making, Stochastic processes,
Focusing, Encoding
BibRef
Ringqvist, C.[Carl],
Butepage, J.[Judith],
Kjellström, H.[Hedvig],
Hult, H.[Henrik],
Interpolation in Auto Encoders with Bridge Processes,
ICPR21(5973-5980)
IEEE DOI
2105
Bridges, Measurement, Legged locomotion, Interpolation,
Stochastic processes, Games, Probability distribution
BibRef
McConville, R.[Ryan],
Santos-Rodríguez, R.[Raúl],
Piechocki, R.J.[Robert J.],
Craddock, I.[Ian],
N2D: (Not Too) Deep Clustering via Clustering the Local Manifold of
an Autoencoded Embedding,
ICPR21(5145-5152)
IEEE DOI
2105
Manifolds, Clustering methods, Neural networks,
Clustering algorithms, Activity recognition, Manifold learning
BibRef
Ji, Q.[Qiang],
Sun, Y.F.[Yan-Feng],
Hu, Y.L.[Yong-Li],
Yin, B.C.[Bao-Cai],
Variational Deep Embedding Clustering by Augmented Mutual Information
Maximization,
ICPR21(2196-2202)
IEEE DOI
2105
Correlation, Clustering methods, Estimation, Robustness, Data mining,
Task analysis, Pattern analysis
BibRef
Khan, R.A.[Rayyan Ahmad],
Anwaar, M.U.[Muhammad Umer],
Kleinsteuber, M.[Martin],
Epitomic Variational Graph Autoencoder,
ICPR21(7203-7210)
IEEE DOI
2105
Benchmark testing, Task analysis, Standards,
Graph auto encoder, Variational graph autoencoder, EVGAE
BibRef
Plumerault, A.[Antoine],
Borgne, H.L.[Hervé Le],
Hudelot, C.[Céline],
AVAE: Adversarial Variational Auto Encoder,
ICPR21(8687-8694)
IEEE DOI
2105
Manifolds, Generative adversarial networks
BibRef
Pihlgren, G.G.[Gustav Grund],
Sandin, F.[Fredrik],
Liwicki, M.[Marcus],
Pretraining Image Encoders without Reconstruction via Feature
Prediction Loss,
ICPR21(4105-4111)
IEEE DOI
2105
Training, Deep learning, Turning, Decoding,
Task analysis, Image reconstruction, Autoencoder, Perceptual,
Embeddings
BibRef
Talafha, S.[Sameerah],
Rekabdar, B.[Banafsheh],
Mousas, C.[Christos],
Ekenna, C.[Chinwe],
Biologically Inspired Sleep Algorithm for Variational Auto-encoders,
ISVC20(I:54-67).
Springer DOI
2103
BibRef
Tran, D.H.[Duc Hoa],
Meunier, M.[Michel],
Cheriet, F.[Farida],
Deep Image Clustering Using Self-learning Optimization in a Variational
Auto-encoder,
DLPR20(736-749).
Springer DOI
2103
BibRef
Bhalodia, R.[Riddhish],
Lee, I.[Iain],
Elhabian, S.[Shireen],
dpvaes: Fixing Sample Generation for Regularized VAEs,
ACCV20(IV:643-660).
Springer DOI
2103
BibRef
Vowels, M.J.[Matthew J.],
Camgoz, N.C.[Necati Cihan],
Bowden, R.[Richard],
VDSM: Unsupervised Video Disentanglement with State-Space Modeling
and Deep Mixtures of Experts,
CVPR21(8172-8182)
IEEE DOI
2111
BibRef
Earlier:
Gated Variational AutoEncoders:
Incorporating Weak Supervision to Encourage Disentanglement,
FG20(125-132)
IEEE DOI
2102
Computational modeling, Machine learning,
Cognition, Pattern recognition, Decoding, Task analysis.
Training, Logic gates, Measurement, Task analysis,
Image reconstruction, Decoding, Faces, VAE, disentanglement,
generative models
BibRef
Purkait, P.[Pulak],
Zach, C.[Christopher],
Reid, I.D.[Ian D.],
SG-VAE: Scene Grammar Variational Autoencoder to Generate New Indoor
Scenes,
ECCV20(XXIV:155-171).
Springer DOI
2012
BibRef
Kim, B.C.,
Kim, J.U.,
Lee, H.,
Ro, Y.M.,
Revisiting Role of Autoencoders in Adversarial Settings,
ICIP20(1856-1860)
IEEE DOI
2011
Robustness, Training, Perturbation methods, Noise reduction, Gold,
Entropy, Image reconstruction, Deep learning,
adversarial example
BibRef
Guo, Z.Y.[Zong-Yu],
Wu, Y.J.[Yao-Jun],
Feng, R.S.[Run-Sen],
Zhang, Z.Z.[Zhi-Zheng],
Chen, Z.B.[Zhi-Bo],
3-D Context Entropy Model for Improved Practical Image Compression,
CLIC20(520-523)
IEEE DOI
2008
VAE Framework for compression.
Context modeling, Solid modeling, Image coding, Entropy, Training,
Transforms, Image resolution
BibRef
Zhang, Z.,
Sun, L.,
Zheng, Z.,
Li, Q.,
Disentangling The Spatial Structure and Style in Conditional VAE,
ICIP20(1626-1630)
IEEE DOI
2011
cVAE, GAN, disentanglement
BibRef
Li, Z.,
Togo, R.,
Ogawa, T.,
Haseyama, M.,
Variational Autoencoder Based Unsupervised Domain Adaptation For
Semantic Segmentation,
ICIP20(2426-2430)
IEEE DOI
2011
Semantics, Task analysis, Adaptation models, Mathematical model,
Linear programming, Training, Learning systems,
adversarial learning
BibRef
Campo, D.,
Slavic, G.,
Baydoun, M.,
Marcenaro, L.,
Regazzoni, C.,
Continual Learning Of Predictive Models In Video Sequences Via
Variational Autoencoders,
ICIP20(753-757)
IEEE DOI
2011
Training, Predictive models, Video sequences, Task analysis,
Technological innovation, Testing, Artificial neural networks,
kalman filter
BibRef
Li, H.[Henry],
Lindenbaum, O.[Ofir],
Cheng, X.Y.[Xiu-Yuan],
Cloninger, A.[Alexander],
Variational Diffusion Autoencoders with Random Walk Sampling,
ECCV20(XXIII:362-378).
Springer DOI
2011
BibRef
Wannenwetsch, A.S.[Anne S.],
Roth, S.[Stefan],
Probabilistic Pixel-Adaptive Refinement Networks,
CVPR20(11639-11648)
IEEE DOI
2008
Picture archiving and communication systems,
Probabilistic logic, Uncertainty, Reliability, Optical imaging,
Task analysis
BibRef
Theodoridis, T.[Thomas],
Chatzis, T.[Theocharis],
Solachidis, V.[Vassilios],
Dimitropoulos, K.[Kosmas],
Daras, P.[Petros],
Cross-modal Variational Alignment of Latent Spaces,
MULWS20(4127-4136)
IEEE DOI
2008
Two variational autoencoder (VAE) networks which generate and model
the latent space of each modality.
Decoding, Task analysis,
Probability distribution, Training, Pose estimation
BibRef
Yuan, Y.,
Lai, Y.,
Yang, J.,
Duan, Q.,
Fu, H.,
Gao, L.,
Mesh Variational Autoencoders with Edge Contraction Pooling,
L3DGM20(1105-1112)
IEEE DOI
2008
Shape, Strain, Interpolation,
Neural networks, Machine learning
BibRef
Han, T.,
Nijkamp, E.,
Zhou, L.,
Pang, B.,
Zhu, S.,
Wu, Y.N.,
Joint Training of Variational Auto-Encoder and Latent Energy-Based
Model,
CVPR20(7975-7984)
IEEE DOI
2008
Generators, Training, Data models, Linear programming,
Minimization, Computational modeling
BibRef
Yadav, R.[Ravindra],
Sardana, A.[Ashish],
Namboodiri, V.P.[Vinay P.],
Hegde, R.M.[Rajesh M.],
Bridged Variational Autoencoders for Joint Modeling of Images and
Attributes,
WACV20(1468-1476)
IEEE DOI
2006
Training, Decoding, Computational modeling, Data models, Bridges,
Task analysis, Computer architecture
BibRef
Liu, W.,
Li, R.,
Zheng, M.,
Karanam, S.,
Wu, Z.,
Bhanu, B.,
Radke, R.J.,
Camps, O.,
Towards Visually Explaining Variational Autoencoders,
CVPR20(8639-8648)
IEEE DOI
2008
Visualization, Image reconstruction, Standards, Task analysis,
Computational modeling, Anomaly detection, Predictive models
BibRef
Gupta, K.[Kamal],
Singh, S.[Saurabh],
Shrivastava, A.[Abhinav],
PatchVAE: Learning Local Latent Codes for Recognition,
CVPR20(4745-4754)
IEEE DOI
2008
Task analysis, Data models, Image reconstruction, Visualization,
Standards, Computer architecture, Decoding
BibRef
Zhang, J.,
Fan, D.,
Dai, Y.,
Anwar, S.,
Saleh, F.S.,
Zhang, T.,
Barnes, N.,
UC-Net: Uncertainty Inspired RGB-D Saliency Detection via Conditional
Variational Autoencoders,
CVPR20(8579-8588)
IEEE DOI
2008
Saliency detection, Labeling, Predictive models, Uncertainty,
Pipelines, Probabilistic logic, Training
BibRef
Vowels, M.J.,
Cihan Camgöz, N.,
Bowden, R.,
NestedVAE: Isolating Common Factors via Weak Supervision,
CVPR20(9199-9209)
IEEE DOI
2008
Task analysis, Decoding, Data models, Image reconstruction,
Measurement, Computational modeling
BibRef
Yang, L.,
Cheung, N.,
Li, J.,
Fang, J.,
Deep Clustering by Gaussian Mixture Variational Autoencoders With
Graph Embedding,
ICCV19(6439-6448)
IEEE DOI
2004
Code, Graph Embedding.
WWW Link. data structures, feature extraction, Gaussian processes,
graph theory, learning (artificial intelligence), minimisation,
Gaussian mixture model
BibRef
Zhu, Y.Z.[Yi-Zhe],
Min, M.R.[Martin Renqiang],
Kadav, A.[Asim],
Graf, H.P.[Hans Peter],
S3VAE: Self-Supervised Sequential VAE for Representation
Disentanglement and Data Generation,
CVPR20(6537-6546)
IEEE DOI
2008
Sequential Variational Autoencoder.
Static factors and dynamic factors.
Videos, Data models, Task analysis, Visualization, Dynamics,
Computational modeling
BibRef
Ding, Z.[Zheng],
Xu, Y.[Yifan],
Xu, W.J.[Wei-Jian],
Parmar, G.[Gaurav],
Yang, Y.[Yang],
Welling, M.[Max],
Tu, Z.W.[Zhuo-Wen],
Guided Variational Autoencoder for Disentanglement Learning,
CVPR20(7917-7926)
IEEE DOI
2008
Principal component analysis, Task analysis, Decoding, Training,
Standards, Image reconstruction
BibRef
Zheng, Z.L.[Zhi-Lin],
Sun, L.[Li],
Disentangling Latent Space for VAE by Label Relevant/Irrelevant
Dimensions,
CVPR19(12184-12193).
IEEE DOI
2002
BibRef
Rolinek, M.[Michal],
Zietlow, D.[Dominik],
Martius, G.[Georg],
Variational Autoencoders Pursue PCA Directions (by Accident),
CVPR19(12398-12407).
IEEE DOI
2002
BibRef
Han, Z.,
Wang, X.,
Liu, Y.,
Zwicker, M.,
Multi-Angle Point Cloud-VAE: Unsupervised Feature Learning for 3D
Point Clouds From Multiple Angles by Joint Self-Reconstruction and
Half-to-Half Prediction,
ICCV19(10441-10450)
IEEE DOI
2004
computational geometry, feature extraction,
unsupervised learning, shape generation, RNN, MAP-VAE,
BibRef
Zhu, Y.,
Suri, S.,
Kulkarni, P.,
Chen, Y.,
Duan, J.,
Kuo, C.C.J.,
An Interpretable Generative Model for Handwritten Digits Synthesis,
ICIP19(1910-1914)
IEEE DOI
1910
Generative model, feedforward Design,
variational autoencoder (VAE), explainable machine learning,
principal component analysis (PCA)
BibRef
Kingkan, C.,
Hashimoto, C.,
Generating Mesh-based Shapes From Learned Latent Spaces of Point
Clouds with VAE-GAN,
ICPR18(308-313)
IEEE DOI
1812
computational geometry, encoding, image reconstruction,
image representation, learning (artificial intelligence),
Image reconstruction
BibRef
Felsen, P.[Panna],
Lucey, P.[Patrick],
Ganguly, S.[Sujoy],
Where Will They Go? Predicting Fine-Grained Adversarial Multi-agent
Motion Using Conditional Variational Autoencoders,
ECCV18(XI: 761-776).
Springer DOI
1810
BibRef
Xian, Y.Q.[Yong-Qin],
Sharma, S.[Saurabh],
Schiele, B.[Bernt],
Akata, Z.[Zeynep],
F-VAEGAN-D2: A Feature Generating Framework for Any-Shot Learning,
CVPR19(10267-10276).
IEEE DOI
2002
BibRef
Shang, W.L.[Wen-Ling],
Sohn, K.[Kihyuk],
Tian, Y.D.[Yuan-Dong],
Channel-Recurrent Autoencoding for Image Modeling,
WACV18(1195-1204)
IEEE DOI
1806
feature extraction, image representation, image resolution, VAEs,
Variational Autoencoders, adversarial loss, bedrooms,
Training
BibRef
Bidart, R.[Rene],
Wong, A.[Alexander],
Affine Variational Autoencoders,
ICIAR19(I:461-472).
Springer DOI
1909
BibRef
Mishra, A.,
Reddy, S.K.,
Mittal, A.,
Murthy, H.A.,
A Generative Model for Zero Shot Learning Using Conditional
Variational Autoencoders,
Scarce18(2269-22698)
IEEE DOI
1812
Semantics, Training, Image generation, Decoding, Visualization, Data models
BibRef
Jyothi, A.A.[Akash Abdu],
Durand, T.[Thibaut],
He, J.W.[Jia-Wei],
Sigal, L.[Leonid],
Mori, G.[Greg],
LayoutVAE: Stochastic Scene Layout Generation From a Label Set,
ICCV19(9894-9903)
IEEE DOI
2004
object detection, LayoutVAE, label set, textual description,
plausible visual variations,
BibRef
Jin, G.,
Zhang, D.,
Dai, F.,
Guo, J.,
Ma, Y.,
Zhang, Y.,
Semantic Preserving Hash Coding Through VAE-GAN,
ICIP18(1997-2001)
IEEE DOI
1809
Semantics, Generators, Machine learning, Training, Binary codes,
Image generation, Image retrieval,
Generative adversarial network
BibRef
Kim, M.Y.[Min-Young],
Wang, Y.T.[Yu-Ting],
Sahu, P.[Pritish],
Pavlovic, V.[Vladimir],
Bayes-Factor-VAE: Hierarchical Bayesian Deep Auto-Encoder Models for
Factor Disentanglement,
ICCV19(2979-2987)
IEEE DOI
2004
Bayes methods, Gaussian processes, inference mechanisms,
learning (artificial intelligence), latent dimensions,
Maximum likelihood estimation
BibRef
Tan, Q.,
Gao, L.,
Lai, Y.,
Xia, S.,
Variational Autoencoders for Deforming 3D Mesh Models,
CVPR18(5841-5850)
IEEE DOI
1812
Shape, Solid modeling, Deformable models, Analytical models, Geometry
BibRef
Yoo, Y.,
Yun, S.,
Chang, H.J.,
Demiris, Y.,
Choi, J.Y.,
Variational Autoencoded Regression:
High Dimensional Regression of Visual Data on Complex Manifold,
CVPR17(2943-2952)
IEEE DOI
1711
Data models, Decoding, Gaussian processes, Image reconstruction,
Image sequences, Kernel, Visualization
BibRef
Abbasnejad, M.E.,
Dick, A.,
van den Hengel, A.J.[Anton J.],
Infinite Variational Autoencoder for Semi-Supervised Learning,
CVPR17(781-790)
IEEE DOI
1711
Bayes methods, Data models, Mixture models, Predictive models,
Semisupervised learning, Supervised learning, Training
BibRef
Goyal, P.,
Hu, Z.,
Liang, X.,
Wang, C.,
Xing, E.P.,
Mellon, C.,
Nonparametric Variational Auto-Encoders for Hierarchical
Representation Learning,
ICCV17(5104-5112)
IEEE DOI
1802
Bayes methods, image representation, inference mechanisms,
learning (artificial intelligence), neural nets,
Standards
BibRef
Chapter on Pattern Recognition, Clustering, Statistics, Grammars, Learning, Neural Nets, Genetic Algorithms continues in
Bayesian Learning, Bayes Network, Bayesian Networks .