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1506
Analytical models. Learning models.
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Wang, Y.,
Autoencoder With Invertible Functions for Dimension Reduction and
Image Reconstruction,
SMCS(48), No. 7, July 2018, pp. 1065-1079.
IEEE DOI
1806
Artificial neural networks, Decoding, Encoding,
Image reconstruction, Learning systems, Nonhomogeneous media,
generalization performance
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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)
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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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Mixture variational autoencoders,
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Elsevier DOI
1912
Mixture variational autoencoders, Mixture models,
Reparameterization trick, SGVB estimator
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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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Deep Clustering With Variational Autoencoder,
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IEEE DOI
2002
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Xie, W.,
Yang, J.,
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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)
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Shao, J.[Jie],
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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.,
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)
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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
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Milani, S.[Simone],
A distributed source autoencoder of local visual descriptors for 3D
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PRL(146), 2021, pp. 193-199.
Elsevier DOI
2105
Distributed vision networks, Distributed source coding,
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Kamikawa, Y.[Yuta],
Hashimoto, A.[Atsushi],
Sonogashira, M.[Motoharu],
Iiyama, M.[Masaaki],
Curiosity Guided Fine-Tuning for Encoder-Decoder-Based Visual
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IEICE(E104-D), No. 5, May 2021, pp. 752-761.
WWW Link.
2105
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Takahashi, R.[Ryuhei],
Hashimoto, A.[Atsushi],
Sonogashira, M.[Motoharu],
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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
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SPLetters(28), 2021, pp. 987-991.
IEEE DOI
2106
Training, Task analysis, Decoding, Mathematical model,
Signal to noise ratio, Image reconstruction, Estimation,
Representation learning
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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
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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
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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.S.[Xue-Song],
Wang, M.T.[Man-Tao],
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
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Zhang, J.[Jing],
Fan, D.P.[Deng-Ping],
Dai, Y.C.[Yu-Chao],
Anwar, S.[Saeed],
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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
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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
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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
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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
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Zhang, C.X.[Chen-Xi],
Zhao, H.[Huiliang],
Chen, W.C.[Wen-Chao],
Chen, B.[Bo],
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Jia, C.[Changrui],
Liu, H.W.[Hong-Wei],
Robust Multiple-Measurement Sparsity-Aware STAP with Bayesian
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2208
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Learning intra-domain style-invariant representation for unsupervised
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Elsevier DOI
2209
BibRef
Earlier:
Variational Autoencoder Based Unsupervised Domain Adaptation For
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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
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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
Fujii, K.[Keisuke],
Kawahara, Y.[Yoshinobu],
Supervised dynamic mode decomposition via multitask learning,
PRL(122), 2019, pp. 7-13.
Elsevier DOI
1904
Dynamical systems, Dimensionality reduction,
Feature extraction, Dynamic mode decomposition
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Ul Haq, I.[Israr],
Fujii, K.[Keisuke],
Kawahara, Y.[Yoshinobu],
Dynamic mode decomposition via dictionary learning for foreground
modeling in videos,
CVIU(199), 2020, pp. 103022.
Elsevier DOI
2009
Dynamic mode decomposition, Nonlinear dynamical system,
Dictionary learning, Object extraction, Background modeling,
Foreground modeling
BibRef
Ul Haq, I.[Israr],
Iwata, T.[Tomoharu],
Kawahara, Y.[Yoshinobu],
Dynamic mode decomposition via convolutional autoencoders for
dynamics modeling in videos,
CVIU(216), 2022, pp. 103355.
Elsevier DOI
2202
Dynamical systems, Dynamic mode decomposition,
Nonlinear dynamics, Foreground modeling, Background modeling, Video classification
BibRef
Kamal, I.M.[Imam Mustafa],
Bae, H.[Hyerim],
Cooperative auto-classifier networks for boosting discriminant
capacity,
PRL(160), 2022, pp. 82-89.
Elsevier DOI
2208
Classification, Dimensionality reduction, Autoencoder,
Pattern recognition, Cooperative neural networks
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
Zheng, Y.[Yimei],
Jia, C.Y.[Cai-Yan],
Yu, J.[Jian],
Li, X.Y.[Xuan-Ya],
Deep embedded clustering with distribution consistency preservation
for attributed networks,
PR(139), 2023, pp. 109469.
Elsevier DOI
2304
Deep embedded clustering, Autoencoder, Graph autoencoder,
Node representation learning, Cluster distribution consistency
BibRef
Liu, Y.[Yue],
Liu, Z.[Zitu],
Li, S.[Shuang],
Yu, Z.Y.[Zhen-Yao],
Guo, Y.[Yike],
Liu, Q.[Qun],
Wang, G.[Guoyin],
Cloud-VAE: Variational autoencoder with concepts embedded,
PR(140), 2023, pp. 109530.
Elsevier DOI
2305
Variational autoencoder, Disentangled representation,
Concept embedded, Cloud Model, Deep Learning Interpretability
BibRef
Duque, A.F.[Andres F.],
Morin, S.[Sacha],
Wolf, G.[Guy],
Moon, K.R.[Kevin R.],
Geometry Regularized Autoencoders,
PAMI(45), No. 6, June 2023, pp. 7381-7394.
IEEE DOI
2305
Kernel, Geometry, Manifold learning, Data visualization, Manifolds,
Training, Principal component analysis, Autoencoders,
semi-supervised learning
BibRef
Chattopadhyay, A.[Aditya],
Zhang, X.[Xi],
Wipf, D.P.[David Paul],
Arora, H.[Himanshu],
Vidal, R.[René],
Learning Graph Variational Autoencoders with Constraints and
Structured Priors for Conditional Indoor 3D Scene Generation,
WACV23(785-794)
IEEE DOI
2302
Training, Adaptation models, Solid modeling, Shape, Databases, Layout,
Algorithms: Image recognition and understanding (object detection,
image and video synthesis
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.F.[Yi-Fei],
Lin, H.H.[Hao-Hong],
Lin, L.Z.[Long-Zhong],
Chen, Y.Z.[Yi-Zhuo],
Yang, Q.M.[Qin-Min],
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],
Piurica, 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
Gadirov, H.[Hamid],
Tkachev, G.[Gleb],
Ertl, T.[Thomas],
Frey, S.[Steffen],
Evaluation and Selection of Autoencoders for Expressive Dimensionality
Reduction of Spatial Ensembles,
ISVC21(I:222-234).
Springer DOI
2112
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
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
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
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
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
BibRef
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
Mohbat,
Mukhtar, T.[Tooba],
Khurshid, N.[Numan],
Taj, M.[Murtaza],
Dimensionality Reduction Using Discriminative Autoencoders for Remote
Sensing Image Retrieval,
CIAP19(I:499-508).
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
Wang, W.[Wei],
Huang, Y.[Yan],
Wang, Y.Z.[Yi-Zhou],
Wang, L.[Liang],
Generalized Autoencoder:
A Neural Network Framework for Dimensionality Reduction,
DeepLearn14(496-503)
IEEE DOI
1409
Autoencoder; Deep learning; Dimensionality reduction
BibRef
Chapter on Pattern Recognition, Clustering, Statistics, Grammars, Learning, Neural Nets, Genetic Algorithms continues in
Generative Autoencoder .