14.5.8.8.2 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


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, Computer vision 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

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

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, Computer vision 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:Sep 14, 2020 at 15:32:18