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Zhu, J.[Jun],
Gläscher, J.[Jan],
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Elsevier DOI
1909
VAE, Image caption, Generative models, Semi-supervised
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Duan, X.T.[Xin-Tao],
Liu, J.J.[Jing-Jing],
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Efficient image encryption and compression based on a VAE generative
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Su, Y.,
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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.,
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Adaptive Augmentation of Medical Data Using Independently Conditional
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MedImg(38), No. 12, December 2019, pp. 2807-2820.
IEEE DOI
1912
Training, Data models, Biomedical imaging, Adaptation models,
Image segmentation, Feature extraction, Computational modeling,
tumor segmentation
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Jiang, S.[Shuoran],
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Mixture variational autoencoders,
PRL(128), 2019, pp. 263-269.
Elsevier DOI
1912
Mixture variational autoencoders, Mixture models,
Reparameterization trick, SGVB estimator
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Kossyk, I.[Ingo],
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Discriminative regularization of the latent manifold of variational
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JVCIR(61), 2019, pp. 121-129.
Elsevier DOI
1906
Variational auto-encoder, Regularization,
Knowledge representation, Perceptual data compaction,
Statistical performance analysis
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Liu, S.[Shiqi],
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Discovering influential factors in variational autoencoders,
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2005
Variational autoencoder, Mutual information, Generative model
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Lim, K.L.[Kart-Leong],
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Deep Clustering With Variational Autoencoder,
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IEEE DOI
2002
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Joo, W.Y.[Weon-Young],
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Dirichlet Variational Autoencoder,
PR(107), 2020, pp. 107514.
Elsevier DOI
2008
Representation learning, Variational autoencoder,
Deep generative model, Multi-modal latent representation, Component collapse
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Xie, W.,
Yang, J.,
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Du, Q.,
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SRUN: Spectral Regularized Unsupervised Networks for Hyperspectral
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GeoRS(58), No. 2, February 2020, pp. 1463-1474.
IEEE DOI
2001
Feature extraction, Object detection, Hyperspectral imaging,
Anomaly detection, Decoding, Aircraft, Background suppression,
variational autoencoders (VAEs)
BibRef
Nazábal, A.[Alfredo],
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Handling incomplete heterogeneous data using VAEs,
PR(107), 2020, pp. 107501.
Elsevier DOI
2008
Generative models, Variational autoencoders, Incomplete heterogenous data
BibRef
Gao, R.,
Hou, X.,
Qin, J.,
Chen, J.,
Liu, L.,
Zhu, F.,
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
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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],
Yin, J.[Jian],
Yang, W.W.[Wei-Wei],
Li, B.[Bo],
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],
Hernández-Lobato, J.M.[José Miguel],
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],
Fox-Roberts, P.[Patrick],
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Y-Autoencoders: Disentangling latent representations via sequential
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Elsevier DOI
2012
Disentangled representations, Deep learning, Autoencoders, Generative models
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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
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
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 .