14.5.9.9.4 VAE, Variational Autoencoder

Chapter Contents (Back)
VAE. Autoencoder.

Zakharov, N.[Nikolai], Su, H.[Hang], Zhu, J.[Jun], Gläscher, J.[Jan],
Towards controllable image descriptions with semi-supervised VAE,
JVCIR(63), 2019, pp. 102574.
Elsevier DOI 1909
VAE, Image caption, Generative models, Semi-supervised BibRef

Duan, X.T.[Xin-Tao], Liu, J.J.[Jing-Jing], Zhang, E.[En],
Efficient image encryption and compression based on a VAE generative model,
RealTimeIP(16), No. 3, June 2019, pp. 765-773.
WWW Link. 1906
BibRef

Su, Y., Li, J., Plaza, A., Marinoni, A., Gamba, P., Chakravortty, S.,
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, variational autoencoder (VAE) BibRef

Pesteie, M., Abolmaesumi, P., Rohling, R.N.,
Adaptive Augmentation of Medical Data Using Independently Conditional Variational Auto-Encoders,
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 BibRef

Jiang, S.[Shuoran], Chen, Y.[Yarui], Yang, J.[Jucheng], Zhang, C.[Chuanlei], Zhao, T.T.[Ting-Ting],
Mixture variational autoencoders,
PRL(128), 2019, pp. 263-269.
Elsevier DOI 1912
Mixture variational autoencoders, Mixture models, Reparameterization trick, SGVB estimator BibRef

Kossyk, I.[Ingo], Márton, Z.C.[Zoltán-Csaba],
Discriminative regularization of the latent manifold of variational auto-encoders,
JVCIR(61), 2019, pp. 121-129.
Elsevier DOI 1906
Variational auto-encoder, Regularization, Knowledge representation, Perceptual data compaction, Statistical performance analysis BibRef

Liu, S.[Shiqi], Liu, J.X.[Jing-Xin], Zhao, Q.[Qian], Cao, X.Y.[Xiang-Yong], Li, H.B.[Hui-Bin], Meng, D.Y.[De-Yu], Meng, H.Y.[Hong-Ying], Liu, S.[Sheng],
Discovering influential factors in variational autoencoders,
PR(100), 2020, pp. 107166.
Elsevier DOI 2005
Variational autoencoder, Mutual information, Generative model BibRef

Lim, K.L.[Kart-Leong], Jiang, X.D.[Xu-Dong], Yi, C.Y.[Chen-Yu],
Deep Clustering With Variational Autoencoder,
SPLetters(27), 2020, pp. 231-235.
IEEE DOI 2002
BibRef

Joo, W.Y.[Weon-Young], Lee, W.S.[Won-Sung], Park, S.[Sungrae], Moon, I.C.[Il-Chul],
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., Lei, J., Li, Y., Du, Q., He, G.,
SRUN: Spectral Regularized Unsupervised Networks for Hyperspectral Target Detection,
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], Olmos, P.M.[Pablo M.], Ghahramani, Z.[Zoubin], Valera, I.[Isabel],
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., Zhang, Z., Shao, L.,
Zero-VAE-GAN: Generating Unseen Features for Generalized and Transductive Zero-Shot Learning,
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 encoding,
PRL(140), 2020, pp. 59-65.
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 Forecasting,
IEICE(E104-D), No. 5, May 2021, pp. 752-761.
WWW Link. 2105
BibRef

Takahashi, R.[Ryuhei], Hashimoto, A.[Atsushi], Sonogashira, M.[Motoharu], Iiyama, M.[Masaaki],
Partially-shared Variational Auto-encoders for Unsupervised Domain Adaptation with Target Shift,
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.T.[Zhi-Tao], 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

Sun, J.[Jun], Zhang, J.[Junbo], Gao, X.[Xuesong], Wang, M.[Mantao], Ou, D.H.[Ding-Hua], Wu, X.B.[Xiao-Bo], Zhang, D.J.[De-Jun],
Fusing Spatial Attention with Spectral-Channel Attention Mechanism for Hyperspectral Image Classification via Encoder-Decoder Networks,
RS(14), No. 9, 2022, pp. xx-yy.
DOI Link 2205
BibRef

La Grassa, R.[Riccardo], Re, C.[Cristina], Cremonese, G.[Gabriele], Gallo, I.[Ignazio],
Hyperspectral Data Compression Using Fully Convolutional Autoencoder,
RS(14), No. 10, 2022, pp. xx-yy.
DOI Link 2206
BibRef

Zhang, X.X.[Xiao-Xi], Gao, Y.[Yuan], Wang, X.[Xin], Feng, J.[Jun], Shi, Y.[Yan],
GeoSDVA: A Semi-Supervised Dirichlet Variational Autoencoder Model for Transportation Mode Identification,
IJGI(11), No. 5, 2022, pp. xx-yy.
DOI Link 2206
BibRef

Fan, H.[Haoyi], Zhang, F.B.[Feng-Bin], Wei, Y.X.[Yu-Xuan], Li, Z.Y.[Zuo-Yong], Zou, C.Q.[Chang-Qing], Gao, Y.[Yue], Dai, Q.H.[Qiong-Hai],
Heterogeneous Hypergraph Variational Autoencoder for Link Prediction,
PAMI(44), No. 8, August 2022, pp. 4125-4138.
IEEE DOI 2207
Semantics, Predictive models, Task analysis, Topology, Stochastic processes, Network topology, Fans, variational inference BibRef

Xu, W.J.[Wen-Jia], Xian, Y.Q.[Yong-Qin], Wang, J.[Jiuniu], Schiele, B.[Bernt], Akata, Z.[Zeynep],
Attribute Prototype Network for Any-Shot Learning,
IJCV(130), No. 7, July 2022, pp. 1735-1753.
Springer DOI 2207
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

Zhang, J.[Jing], Fan, D.P.[Deng-Ping], Dai, Y.[Yuchao], Anwar, S.[Saeed], Saleh, F.S.[Fatemeh S.], Aliakbarian, S.[Sadegh], Barnes, N.[Nick],
Uncertainty Inspired RGB-D Saliency Detection,
PAMI(44), No. 9, September 2022, pp. 5761-5779.
IEEE DOI 2208
Saliency detection, Predictive models, Uncertainty, Pipelines, Data models, Labeling, Training, Uncertainty, alternating back-propagation BibRef

Zhang, J.[Jing], Fan, D.P.[Deng-Ping], Dai, Y.[Yuchao], Anwar, S.[Saeed], Saleh, F.S.[Fatemeh S.], Zhang, T., Barnes, N.[Nick],
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

Chien, J.T.[Jen-Tzung], Wang, C.W.[Chun-Wei],
Hierarchical and Self-Attended Sequence Autoencoder,
PAMI(44), No. 9, September 2022, pp. 4975-4986.
IEEE DOI 2208
Decoding, Stochastic processes, Training, Semantics, Recurrent neural networks, Natural languages, Data models, self attention BibRef


Ye, F.[Fei], Bors, A.G.[Adrian G.],
Mixtures of Variational Autoencoders,
IPTA20(1-6)
IEEE DOI 2206
Deep learning, Training, Manifolds, Mixture models, Tools, Data models, Task analysis, Mixture models, Variational autoencoder, Hilbert-Schmidt Independence Criterion BibRef

Abukmeil, M.[Mohanad], Ferrari, S.[Stefano], Genovese, A.[Angelo], Piuri, V.[Vincenzo], Scotti, F.[Fabio],
Grad2 VAE: An Explainable Variational Autoencoder Model Based on Online Attentions Preserving Curvatures of Representations,
CIAP22(I:670-681).
Springer DOI 2205
BibRef

Bastos, I.L.O.[Igor L. O.], Melo, V.H.C.[Victor H. C.], Prates, R.F.[Raphael F.], Schwartz, W.R.[William R.],
DASP: Dual-autoencoder Architecture for Skin Prediction,
CIAP22(II:429-441).
Springer DOI 2205
BibRef

Xu, J.[Jie], Ren, Y.Z.[Ya-Zhou], Tang, H.Y.[Hua-Yi], Pu, X.R.[Xiao-Rong], Zhu, X.F.[Xiao-Feng], Zeng, M.[Ming], He, L.F.[Li-Fang],
Multi-VAE: Learning Disentangled View-common and View-peculiar Visual Representations for Multi-view Clustering,
ICCV21(9214-9223)
IEEE DOI 2203
Visualization, Fuses, Gaussian distribution, Complexity theory, Mutual information, Representation learning 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], Pižurica, 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

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

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 .


Last update:Aug 11, 2022 at 11:48:53