14.5.10.9.5 VAE, Variational Autoencoder

Chapter Contents (Back)
VAE. Autoencoder.
See also Generative Autoencoder.

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

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

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

Shao, J.[Jie], Li, X.R.[Xiao-Rui],
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

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

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

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.Y.[Hao-Yi], 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

Zhang, J.[Jing], Fan, D.P.[Deng-Ping], Dai, Y.C.[Yu-Chao], 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.C.[Yu-Chao], 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

Heiser, Y.[Yaron], Stern, A.[Adrian],
Learned Design of a Compressive Hyperspectral Imager for Remote Sensing by a Physics-Constrained Autoencoder,
RS(14), No. 15, 2022, pp. xx-yy.
DOI Link 2208
BibRef

Zhang, C.X.[Chen-Xi], Zhao, H.[Huiliang], Chen, W.C.[Wen-Chao], Chen, B.[Bo], Wang, P.[Penghui], Jia, C.[Changrui], Liu, H.W.[Hong-Wei],
Robust Multiple-Measurement Sparsity-Aware STAP with Bayesian Variational Autoencoder,
RS(14), No. 15, 2022, pp. xx-yy.
DOI Link 2208
BibRef

Li, Z.Y.[Zong-Yao], Togo, R.[Ren], Ogawa, T.[Takahiro], Haseyama, M.[Miki],
Learning intra-domain style-invariant representation for unsupervised domain adaptation of semantic segmentation,
PR(132), 2022, pp. 108911.
Elsevier DOI 2209
BibRef
Earlier:
Variational Autoencoder Based Unsupervised Domain Adaptation For Semantic Segmentation,
ICIP20(2426-2430)
IEEE DOI 2011
Style-invariant representation, Self-ensembling, Domain adaptation. Semantics, Task analysis, Adaptation models, Mathematical model, Linear programming, Training, Learning systems, adversarial learning BibRef

Li, Z.Y.[Zong-Yao], Togo, R.[Ren], Ogawa, T.[Takahiro], Haseyama, M.[Miki],
Union-Set Multi-source Model Adaptation for Semantic Segmentation,
ECCV22(XXIX:579-595).
Springer DOI 2211
BibRef
And:
Improving Model Adaptation for Semantic Segmentation by Learning Model-Invariant Features with Multiple Source-Domain Models,
ICIP22(421-425)
IEEE DOI 2211
Representation learning, Adaptation models, Training data, Data models, Task analysis, Knowledge transfer, Model adaptation, semantic segmentation BibRef

Li, T.J.[Tian-Jiao], Zhao, X.M.[Xing-Ming], Li, L.M.[Li-Min],
Co-VAE: Drug-Target Binding Affinity Prediction by Co-Regularized Variational Autoencoders,
PAMI(44), No. 12, December 2022, pp. 8861-8873.
IEEE DOI 2212
Drugs, Predictive models, Feature extraction, Computational modeling, Mathematical models, Proteins, drug-target binding affinity BibRef

Li, X.L.[Xue-Long], Zhang, H.Y.[Hong-Yuan], Zhang, R.[Rui],
Adaptive Graph Auto-Encoder for General Data Clustering,
PAMI(44), No. 12, December 2022, pp. 9725-9732.
IEEE DOI 2212
Neural networks, Convolution, Adaptation models, Decoding, Data models, Task analysis, Clustering methods, scalable methods BibRef

Qian, D.[Dong], Cheung, W.K.[William K.],
Learning Hierarchical Variational Autoencoders With Mutual Information Maximization for Autoregressive Sequence Modeling,
PAMI(45), No. 2, February 2023, pp. 1949-1962.
IEEE DOI 2301
Decoding, Data models, Training, Mutual information, Computational modeling, Task analysis, Predictive models, neural autoregressive sequence modeling BibRef

Arumugam, D.[Dharanidharan], Kiran, R.[Ravi],
Interpreting denoising autoencoders with complex perturbation approach,
PR(136), 2023, pp. 109212.
Elsevier DOI 2301
Complex step derivative approximation, Saliency maps, Trustworthiness, Pixel attributions, Sanity checks and deep neural networks (DNNs) BibRef


Morishita, S.[Shumpei], Kudo, M.[Mineichi], Kimura, K.[Keigo], Sun, L.[Lu],
Realization of Autoencoders by Kernel Methods,
SSSPR22(1-10).
Springer DOI 2301
BibRef

Fadlallah, S.[Sarah], Juliŕ, C.[Carme], Serratosa, F.[Francesc],
Graph Regression Based on Graph Autoencoders,
SSSPR22(142-151).
Springer DOI 2301
BibRef

Grujicic, D.[Dusan], Blaschko, M.B.[Matthew B.],
2-D latent space models: Layer-wise perceptual training and spatial grounding,
ICPR22(2437-2443)
IEEE DOI 2212
Training, Manifolds, Satellites, Navigation, Grounding, Data models BibRef

Gangloff, H.[Hugo], Pham, M.T.[Minh-Tan], Courtrai, L.[Luc], Lefčvre, S.[Sébastien],
Leveraging Vector-Quantized Variational Autoencoder Inner Metrics for Anomaly Detection,
ICPR22(435-441)
IEEE DOI 2212
Measurement, Training, Learning systems, Intrusion detection, Machine learning, Medical diagnosis BibRef

da Cruz, S.D.[Steve Dias], Taetz, B.[Bertram], Stifter, T.[Thomas], Stricker, D.[Didier],
Autoencoder Attractors for Uncertainty Estimation,
ICPR22(2553-2560)
IEEE DOI 2212
Training, Visualization, Uncertainty, Computational modeling, Estimation, Training data, Safety BibRef

Pang, Y.[Yatian], Wang, W.X.[Wen-Xiao], Tay, F.E.H.[Francis E. H.], Liu, W.[Wei], Tian, Y.H.[Yong-Hong], Yuan, L.[Li],
Masked Autoencoders for Point Cloud Self-Supervised Learning,
ECCV22(II:604-621).
Springer DOI 2211
BibRef

Yeats, E.[Eric], Liu, F.[Frank], Womble, D.[David], Li, H.[Hai],
NashAE: Disentangling Representations Through Adversarial Covariance Minimization,
ECCV22(XXVII:36-51).
Springer DOI 2211
BibRef

Ye, F.[Fei], Bors, A.G.[Adrian G.],
Continual Variational Autoencoder Learning via Online Cooperative Memorization,
ECCV22(XXIII:531-549).
Springer DOI 2211
BibRef

Chen, Y.[Yabo], Liu, Y.C.[Yu-Chen], Jiang, D.S.[Dong-Sheng], Zhang, X.P.[Xiao-Peng], Dai, W.[Wenrui], Xiong, H.K.[Hong-Kai], Tian, Q.[Qi],
SdAE: Self-distillated Masked Autoencoder,
ECCV22(XXX:108-124).
Springer DOI 2211
BibRef

Dong, X.Y.[Xiao-Yi], Bao, J.[Jianmin], Zhang, T.[Ting], Chen, D.D.[Dong-Dong], Zhang, W.M.[Wei-Ming], Yuan, L.[Lu], Chen, D.[Dong], Wen, F.[Fang], Yu, N.H.[Neng-Hai],
Bootstrapped Masked Autoencoders for Vision BERT Pretraining,
ECCV22(XXX:247-264).
Springer DOI 2211
BibRef

Yang, H.Y.[Hai-Yang], Tang, S.X.[Shi-Xiang], Chen, M.[Meilin], Wang, Y.Z.[Yi-Zhou], Zhu, F.[Feng], Bai, L.[Lei], Zhao, R.[Rui], Ouyang, W.L.[Wan-Li],
Domain Invariant Masked Autoencoders for Self-supervised Learning from Multi-domains,
ECCV22(XXXI:151-168).
Springer DOI 2211
BibRef

Bachmann, R.[Roman], Mizrahi, D.[David], Atanov, A.[Andrei], Zamir, A.[Amir],
MultiMAE: Multi-modal Multi-task Masked Autoencoders,
ECCV22(XXXVII:348-367).
Springer DOI 2211
BibRef

Yamazaki, M.[Meguru], Kora, Y.[Yuichiro], Nakao, T.[Takanori], Lei, X.[Xuying], Yokoo, K.[Kaoru],
Deep Feature Compression using Rate-Distortion Optimization Guided Autoencoder,
ICIP22(1216-1220)
IEEE DOI 2211
Performance evaluation, Video coding, Solid modeling, Image coding, Image edge detection, Rate-distortion, Predictive models, Deep Learning BibRef

Duffhauss, F.[Fabian], Vien, N.A.[Ngo Anh], Ziesche, H.[Hanna], Neumann, G.[Gerhard],
FusionVAE: A Deep Hierarchical Variational Autoencoder for RGB Image Fusion,
ECCV22(XXIX:674-691).
Springer DOI 2211
BibRef

Zou, K.F.[Kai-Feng], Faisan, S.[Sylvain], Heitz, F.[Fabrice], Valette, S.[Sébastien],
Joint Disentanglement of Labels and Their Features with VAE,
ICIP22(1341-1345)
IEEE DOI 2211
Decoding, Image reconstruction, disentangled representation, variational autoencoder BibRef

Muralikrishnan, S.[Sanjeev], Chaudhuri, S.[Siddhartha], Aigerman, N.[Noam], Kim, V.G.[Vladimir G.], Fisher, M.[Matthew], Mitra, N.J.[Niloy J.],
GLASS: Geometric Latent Augmentation for Shape Spaces,
CVPR22(470-479)
IEEE DOI 2210
Training, Geometry, Solid modeling, Adaptation models, Shape, Vision+graphics, Deep learning architectures and techniques BibRef

Wang, G.R.[Guang-Run], Tang, Y.S.[Yan-Song], Lin, L.[Liang], Torr, P.H.S.[Philip H.S.],
Semantic-Aware Auto-Encoders for Self-supervised Representation Learning,
CVPR22(9654-9665)
IEEE DOI 2210
Representation learning, Visualization, Codes, Computational modeling, Semantics, Self-supervised learning, Self- semi- meta- Representation learning BibRef

He, K.M.[Kai-Ming], Chen, X.L.[Xin-Lei], Xie, S.[Saining], Li, Y.[Yanghao], Dollár, P.[Piotr], Girshick, R.[Ross],
Masked Autoencoders Are Scalable Vision Learners,
CVPR22(15979-15988)
IEEE DOI 2210
Training, Couplings, Computational modeling, Data models, Pattern recognition, Representation learning, Self- semi- meta- unsupervised learning BibRef

Anvekar, T.[Tejas], Tabib, R.A.[Ramesh Ashok], Hegde, D.[Dikshit], Mudengudi, U.[Uma],
VG-VAE: A Venatus Geometry Point-Cloud Variational Auto-Encoder,
DLGC22(2977-2984)
IEEE DOI 2210
Geometry, Analytical models, Statistical analysis, Morphology, Benchmark testing, Feature extraction BibRef

Preechakul, K.[Konpat], Chatthee, N.[Nattanat], Wizadwongsa, S.[Suttisak], Suwajanakorn, S.[Supasorn],
Diffusion Autoencoders: Toward a Meaningful and Decodable Representation,
CVPR22(10609-10619)
IEEE DOI 2210
Representation learning, Image synthesis, Semantics, Noise reduction, Stochastic processes, Probabilistic logic, Representation learning BibRef

Chauhan, K.[Kushal], Mohan, U.B.[U. Barath], Shenoy, P.[Pradeep], Gupta, M.[Manish], Sridharan, D.[Devarajan],
Robust outlier detection by de-biasing VAE likelihoods,
CVPR22(9871-9880)
IEEE DOI 2210
Training, Measurement, Computational modeling, Gray-scale, Data models, Pattern recognition, Vision applications and systems BibRef

Kim, M.Y.[Min-Young],
Gaussian Process Modeling of Approximate Inference Errors for Variational Autoencoders,
CVPR22(244-253)
IEEE DOI 2210
Uncertainty, Costs, Computational modeling, Gaussian processes, Network architecture, Benchmark testing, Approximation error, Statistical methods BibRef

Foti, S.[Simone], Koo, B.J.[Bong-Jin], Stoyanov, D.[Danail], Clarkson, M.J.[Matthew J.],
3D Shape Variational Autoencoder Latent Disentanglement via Mini-Batch Feature Swapping for Bodies and Faces,
CVPR22(18709-18718)
IEEE DOI 2210
Solid modeling, Shape, Semantics, Fitting, Training data, Machine learning, Face and gestures, Machine learning, Self- semi- meta- Vision + graphics BibRef

Shao, H.J.[Hua-Jie], Yang, Y.[Yifei], Lin, H.[Haohong], Lin, L.Z.[Long-Zhong], Chen, Y.Z.[Yi-Zhuo], Yang, Q.[Qinmin], Zhao, H.[Han],
Rethinking Controllable Variational Autoencoders,
CVPR22(19228-19237)
IEEE DOI 2210
Representation learning, Training, Annealing, Upper bound, PI control, Image synthesis, Stability analysis, Machine learning 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

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

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

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

He, Z.X.[Zhi-Xun], 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

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
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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
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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

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
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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

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

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, 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

Ding, Z.[Zheng], Xu, Y.F.[Yi-Fan], 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
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Rolinek, M.[Michal], Zietlow, D.[Dominik], Martius, G.[Georg],
Variational Autoencoders Pursue PCA Directions (by Accident),
CVPR19(12398-12407).
IEEE DOI 2002
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

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
Generative Autoencoder .


Last update:Jan 23, 2023 at 16:42:47