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VAE, Image caption, Generative models, Semi-supervised
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1907
Hyperspectral imaging, Estimation, Computers, Training,
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Pesteie, M.,
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Adaptive Augmentation of Medical Data Using Independently Conditional
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IEEE DOI
1912
Training, Data models, Biomedical imaging, Adaptation models,
Image segmentation, Feature extraction, Computational modeling,
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Jiang, S.[Shuoran],
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Elsevier DOI
1912
Mixture variational autoencoders, Mixture models,
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Elsevier DOI
1906
Variational auto-encoder, Regularization,
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Variational autoencoder, Mutual information, Generative model
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IEEE DOI
2002
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Elsevier DOI
2008
Representation learning, Variational autoencoder,
Deep generative model, Multi-modal latent representation, Component collapse
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Xie, W.,
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IEEE DOI
2001
Feature extraction, Object detection, Hyperspectral imaging,
Anomaly detection, Decoding, Aircraft, Background suppression,
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Elsevier DOI
2008
Generative models, Variational autoencoders, Incomplete heterogenous data
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Gao, R.,
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IEEE DOI
2002
Zero-shot learning, generative model, self-training
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Shao, J.,
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Generalized Zero-Shot Learning With Multi-Channel Gaussian Mixture
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IEEE DOI
2004
Visualization, Semantics, Training, Feature extraction,
Gaussian distribution, Cats, Benchmark testing, gaussian mixture VAE
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Wang, X.,
Tan, K.,
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CVA2E: A Conditional Variational Autoencoder With an Adversarial
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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],
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Multi-task learning using variational auto-encoder for sentiment
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PRL(132), 2020, pp. 115-122.
Elsevier DOI
2005
Sentiment classification, Opinion mining, Deep learning,
Multi-task learning, Variational auto-encoder, LSTM, Big data
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Gordon, J.[Jonathan],
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Combining deep generative and discriminative models for Bayesian
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PR(100), 2020, pp. 107156.
Elsevier DOI
2005
Probabilistic models, Semi-supervised learning,
Variational autoencoders, Predictive uncertainty
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Patacchiola, M.[Massimiliano],
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2012
Disentangled representations, Deep learning, Autoencoders, Generative models
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Milani, S.[Simone],
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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],
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Curiosity Guided Fine-Tuning for Encoder-Decoder-Based Visual
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2105
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Takahashi, R.[Ryuhei],
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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],
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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
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Abrol, V.[Vinayak],
Sharma, P.[Pulkit],
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Improving Generative Modelling in VAEs Using Multimodal Prior,
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IEEE DOI
2107
Object oriented modeling, Training, Mutual information, Decoding,
Kernel, Data models, Uncertainty, Generative modelling, autoencoders,
representation learning
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Chen, Z.[Zhitao],
Tong, L.[Lei],
Qian, B.[Bin],
Yu, J.[Jing],
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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],
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Li, F.[Feijiang],
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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
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.[Ian],
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, 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 .