14.5.10.9.8 Generative Autoencoder

Chapter Contents (Back)
Autoencoder. Generative Autoencoder.
See also Adversarial Networks for Image Synthesis, Image Generation.

Xu, W.J.[Wen-Ju], Keshmiri, S.[Shawn], Wang, G.H.[Guang-Hui],
Adversarially Approximated Autoencoder for Image Generation and Manipulation,
MultMed(21), No. 9, September 2019, pp. 2387-2396.
IEEE DOI 1909
Training, Neural networks, Data models, Image reconstruction, Image generation, latent manifold structure BibRef

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

Liu, S.Q.[Shi-Qi], 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

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

Kim, D.J.[Dong-Jun], Song, K.[Kyungwoo], Kim, Y.Y.[Yoon-Yeong], Shin, Y.J.[Yong-Jin], Kang, W.[Wanmo], Moon, I.C.[Il-Chul], Joo, W.Y.[Weon-Young],
Sequential Likelihood-Free Inference with Neural Proposal,
PRL(169), 2023, pp. 102-109.
Elsevier DOI 2305
Likelihood-Free inference, Simulation parameter calibration, MCMC, Generative models 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

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

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

Yu, J.C.[Jun-Chi], Xu, T.Y.[Ting-Yang], 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

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

Ma, S.Q.[Su-Qiang], Liu, C.[Chun], Li, Z.[Zheng], Yang, W.[Wei],
Integrating Adversarial Generative Network with Variational Autoencoders towards Cross-Modal Alignment for Zero-Shot Remote Sensing Image Scene Classification,
RS(14), No. 18, 2022, pp. xx-yy.
DOI Link 2209
BibRef

Choi, J.[Jaewoong], Hwang, G.[Geonho], Kang, M.[Myungjoo],
Disentangling the correlated continuous and discrete generative factors of data,
PR(133), 2023, pp. 109055.
Elsevier DOI 2210
Variational autoencoder, Disentanglement, Generative model, Representation learning BibRef

Peis, I.[Ignacio], Olmos, P.M.[Pablo M.], Artés-Rodríguez, A.[Antonio],
Unsupervised learning of global factors in deep generative models,
PR(134), 2023, pp. 109130.
Elsevier DOI 2212
VAE, Deep generative models, Global factors, Unsupervised learning, Disentanglement, Representation learning BibRef

Shao, H.J.[Hua-Jie], Xiao, Z.S.[Zhi-Sheng], Yao, S.C.[Shuo-Chao], Sun, D.[Dachun], Zhang, A.[Aston], Liu, S.Z.[Sheng-Zhong], Wang, T.[Tianshi], Li, J.Y.[Jin-Yang], Abdelzaher, T.[Tarek],
ControlVAE: Tuning, Analytical Properties, and Performance Analysis,
PAMI(44), No. 12, December 2022, pp. 9285-9297.
IEEE DOI 2212
Image reconstruction, Training, PI control, Optimization, Image synthesis, Task analysis, Tuning, image generation BibRef

Cao, B.[Bing], Bi, Z.W.[Zhi-Wei], Hu, Q.H.[Qing-Hua], Zhang, H.[Han], Wang, N.N.[Nan-Nan], Gao, X.B.[Xin-Bo], Shen, D.G.[Ding-Gang],
AutoEncoder-Driven Multimodal Collaborative Learning for Medical Image Synthesis,
IJCV(131), No. 8, August 2023, pp. 1995-2014.
Springer DOI 2307
BibRef


Yang, H.H.[Hong-Hui], He, T.[Tong], Liu, J.H.[Jia-Heng], Chen, H.[Hua], Wu, B.[Boxi], Lin, B.B.[Bin-Bin], He, X.F.[Xiao-Fei], Ouyang, W.L.[Wan-Li],
GD-MAE: Generative Decoder for MAE Pre-Training on LiDAR Point Clouds,
CVPR23(9403-9414)
IEEE DOI 2309
BibRef

Fei, Z.C.[Zheng-Cong], Fan, M.Y.[Ming-Yuan], Zhu, L.[Li], Huang, J.S.[Jun-Shi], Wei, X.M.[Xiao-Ming], Wei, X.L.[Xiao-Lin],
Masked Auto-Encoders Meet Generative Adversarial Networks and Beyond,
CVPR23(24449-24459)
IEEE DOI 2309
BibRef

Lin, X.[Xinmiao], Li, Y.[Yikang], Hsiao, J.[Jenhao], Ho, C.[Chiuman], Kong, Y.[Yu],
Catch Missing Details: Image Reconstruction with Frequency Augmented Variational Autoencoder,
CVPR23(1736-1745)
IEEE DOI 2309
BibRef

Ma, B.R.[Bao-Rui], Liu, Y.S.[Yu-Shen], Zwicker, M.[Matthias], Han, Z.Z.[Zhi-Zhong],
Surface Reconstruction from Point Clouds by Learning Predictive Context Priors,
CVPR22(6316-6327)
IEEE DOI 2210
Point cloud compression, Measurement, Surface reconstruction, Shape, Benchmark testing, 3D from multi-view and sensors, Vision + graphics BibRef

Han, Z.Z.[Zhi-Zhong], Wang, X., Liu, Y.S.[Yu-Shen], Zwicker, M.[Matthias],
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

Knop, S.[Szymon], Spurek, P.[Przemyslaw], Mazur, M.[Marcin], Tabor, J.[Jacek], Podolak, I.[Igor],
Batch Size Reconstruction-Distribution Trade-Off In Kernel Based Generative Autoencoders,
ICIP22(3728-3732)
IEEE DOI 2211
Training, Codes, Computational modeling, Graphics processing units, Machine learning, Cost function, Data models, deep learning, kernel methods BibRef

Phillips, T.[Tessa], Abdulla, W.[Waleed],
Variational Autoencoders for Generating Hyperspectral Imaging Honey Adulteration Data,
PBVS22(213-220)
IEEE DOI 2210
Training, Training data, Machine learning, Detectors, Pattern recognition 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

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

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

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

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

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

Liang, H.W.[Han-Wen], Zhang, Q.[Qiong], Dai, P.[Peng], Lu, J.W.[Ju-Wei],
Boosting the Generalization Capability in Cross-Domain Few-shot Learning via Noise-enhanced Supervised Autoencoder,
ICCV21(9404-9414)
IEEE DOI 2203
Correlation, Transfer learning, Predictive models, Feature extraction, Boosting, Representation learning BibRef

Ojha, U.[Utkarsh], Li, Y.J.[Yi-Jun], Lu, J.W.[Jing-Wan], Efros, A.A.[Alexei A.], Lee, Y.J.[Yong Jae], Shechtman, E.[Eli], Zhang, R.[Richard],
Few-shot Image Generation via Cross-domain Correspondence,
CVPR21(10738-10747)
IEEE DOI 2111
Training, Image synthesis, Computational modeling, Pattern recognition BibRef

Yoo, J.[Jaeyoung], Lee, H.[Hojun], Kwak, N.[Nojun],
Unpriortized Autoencoder For Image Generation,
ICIP20(763-767)
IEEE DOI 2011
Image reconstruction, Footwear, Image generation, Estimation, Training, Decoding, Interpolation, Autoencoder, Image Generation, Mixture Model 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

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

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

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

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

Chapter on Pattern Recognition, Clustering, Statistics, Grammars, Learning, Neural Nets, Genetic Algorithms continues in
Bayesian Learning, Bayes Network, Bayesian Networks .


Last update:Nov 30, 2023 at 15:51:27