Kamyshanska, H.,
Memisevic, R.,
The Potential Energy of an Autoencoder,
PAMI(37), No. 6, June 2015, pp. 1261-1273.
IEEE DOI
1506
Analytical models. Learning models.
BibRef
Yang, Y.,
Wu, Q.M.J.,
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
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],
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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
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
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
Shao, J.[Jie],
Li, X.R.[Xiao-Rui],
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
Milani, S.[Simone],
A distributed source autoencoder of local visual descriptors for 3D
reconstruction,
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],
Iiyama, M.[Masaaki],
Curiosity Guided Fine-Tuning for Encoder-Decoder-Based Visual
Forecasting,
IEICE(E104-D), No. 5, May 2021, pp. 752-761.
WWW Link.
2105
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
Bansal, V.[Vipul],
Buckchash, H.[Himanshu],
Raman, B.[Balasubramanian],
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
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],
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
BibRef
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,
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.B.[Jun-Bo],
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
BibRef
Zhang, J.[Jing],
Fan, D.P.[Deng-Ping],
Dai, Y.C.[Yu-Chao],
Anwar, S.[Saeed],
Saleh, F.S.[Fatemeh S.],
Aliakbarian, S.[Sadegh],
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
BibRef
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
BibRef
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
BibRef
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
BibRef
Zhang, C.X.[Chen-Xi],
Zhao, H.L.[Hui-Liang],
Chen, W.C.[Wen-Chao],
Chen, B.[Bo],
Wang, P.H.[Peng-Hui],
Jia, C.R.[Chang-Rui],
Liu, H.W.[Hong-Wei],
Robust Multiple-Measurement Sparsity-Aware STAP with Bayesian
Variational Autoencoder,
RS(14), No. 15, 2022, pp. xx-yy.
DOI Link
2208
BibRef
Li, Z.Y.[Zong-Yao],
Togo, R.[Ren],
Ogawa, T.[Takahiro],
Haseyama, M.[Miki],
Learning intra-domain style-invariant representation for unsupervised
domain adaptation of semantic segmentation,
PR(132), 2022, pp. 108911.
Elsevier DOI
2209
BibRef
Earlier:
Variational Autoencoder Based Unsupervised Domain Adaptation For
Semantic Segmentation,
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
BibRef
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
BibRef
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,
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
Cui, Y.F.[Yu-Fei],
Mao, Y.[Yu],
Liu, Z.Q.[Zi-Quan],
Li, Q.[Qiao],
Chan, A.B.[Antoni B.],
Liu, X.[Xue],
Kuo, T.W.[Tei-Wei],
Xue, C.J.[Chun Jason],
Variational Nested Dropout,
PAMI(45), No. 8, August 2023, pp. 10519-10534.
IEEE DOI
2307
Training, Bayes methods, Uncertainty, Indexes, Costs,
Computational modeling, Representation learning,
variational autoencoder
BibRef
Shamsolmoali, P.[Pourya],
Zareapoor, M.[Masoumeh],
Zhou, H.Y.[Hui-Yu],
Tao, D.C.[Da-Cheng],
Li, X.L.[Xue-Long],
VTAE: Variational Transformer Autoencoder With Manifolds Learning,
IP(32), 2023, pp. 4486-4500.
IEEE DOI
2309
BibRef
Ferrante, M.[Matteo],
Boccato, T.[Tommaso],
Spasov, S.[Simeon],
Duggento, A.[Andrea],
Toschi, N.[Nicola],
VAESim: A probabilistic approach for self-supervised prototype
discovery,
IVC(137), 2023, pp. 104746.
Elsevier DOI
2309
Deep clustering, Medical imaging, Variational autoencoders, Prototypes discovery
BibRef
Zou, K.F.[Kai-Feng],
Faisan, S.[Sylvain],
Heitz, F.[Fabrice],
Valette, S.[Sébastien],
Disentangling high-level factors and their features with conditional
vector quantized VAEs,
PRL(172), 2023, pp. 172-180.
Elsevier DOI
2309
Variational autoencoder, Disentangled representation learning, Generative models
BibRef
Zhang, S.C.[Shi-Chen],
Wang, T.L.[Tian-Lei],
Cao, J.W.[Jiu-Wen],
Zhang, W.D.[Wan-Dong],
Chen, B.D.[Ba-Dong],
Matrix randomized autoencoder,
PR(146), 2024, pp. 109992.
Elsevier DOI Code:
WWW Link.
2311
Randomized autoencoder, Matrix representation,
Within-class scatter matrix, Within-class interaction
BibRef
Ojo, A.O.[Akinlolu Oluwabusayo],
Bouguila, N.[Nizar],
A topic modeling and image classification framework:
The Generalized Dirichlet variational autoencoder,
PR(146), 2024, pp. 110037.
Elsevier DOI Code:
WWW Link.
2311
Generalized Dirichlet distribution, Correlation,
Variational autoencoder, Topic models, Reparameterization, Image classification
BibRef
Gu, C.Z.[Chun-Zhi],
Yu, J.[Jun],
Zhang, C.[Chao],
Learning disentangled representations for controllable human motion
prediction,
PR(146), 2024, pp. 109998.
Elsevier DOI
2311
Stochastic motion prediction, Deep generative model, Disentanglement learning
BibRef
Ramchandran, S.[Siddharth],
Tikhonov, G.[Gleb],
Lönnroth, O.[Otto],
Tiikkainen, P.[Pekka],
Lähdesmäki, H.[Harri],
Learning conditional variational autoencoders with missing covariates,
PR(147), 2024, pp. 110113.
Elsevier DOI
2312
Variational autoencoders, Gaussian process, Conditional VAEs,
Missing value imputation
BibRef
Zhou, H.[Hao],
Yang, X.[Xu],
Ren, D.[Dongchun],
Huang, H.[Hai],
Fan, M.Y.[Ming-Yu],
CSIR: Cascaded Sliding CVAEs With Iterative Socially-Aware Rethinking
for Trajectory Prediction,
ITS(24), No. 12, December 2023, pp. 14957-14969.
IEEE DOI
2312
BibRef
An, S.[Seunghwan],
Jeon, J.J.[Jong-June],
Customization of latent space in semi-supervised Variational
AutoEncoder,
PRL(177), 2024, pp. 54-60.
Elsevier DOI
2401
Variational AutoEncoder, Explainable latent space,
Semi-supervised, Customization
BibRef
Ye, F.[Fei],
Bors, A.G.[Adrian. G.],
Self-Supervised Adversarial Variational Learning,
PR(148), 2024, pp. 110156.
Elsevier DOI
2402
BibRef
Earlier:
Continual Variational Autoencoder Learning via Online Cooperative
Memorization,
ECCV22(XXIII:531-549).
Springer DOI
2211
BibRef
Earlier:
Mixtures of Variational Autoencoders,
IPTA20(1-6)
IEEE DOI
2206
Self-supervised learning, Variational Autoencoders (VAE),
Generative Adversarial Nets (GAN), Representation learning, Mutual information.
Deep learning, Training, Manifolds, Mixture models, Tools, Data models,
Task analysis, Mixture models, Variational autoencoder,
Hilbert-Schmidt Independence Criterion
BibRef
Yu, Z.L.[Zi-Long],
Yang, Y.Y.[Yun-Yun],
Zhu, Y.B.[Yong-Bin],
Guo, B.[Bixue],
Li, C.[Chun],
CS-IntroVAE: Cauchy-Schwarz Divergence-Based Introspective
Variational Autoencoder,
MultMed(26), 2024, pp. 663-672.
IEEE DOI
2402
Image reconstruction, Gaussian distribution, Decoding, Training,
Task analysis, Generative adversarial networks,
divergence learning
BibRef
Laakom, F.[Firas],
Raitoharju, J.[Jenni],
Iosifidis, A.[Alexandros],
Gabbouj, M.[Moncef],
Reducing redundancy in the bottleneck representation of autoencoders,
PRL(178), 2024, pp. 202-208.
Elsevier DOI
2402
Autoencoders, Unsupervised learning, Diversity,
Feature representation, Dimensionality reduction, Image compression
BibRef
Wang, Z.Q.[Zhi-Qiang],
Gu, X.J.[Xiao-Jing],
Gu, X.[Xingsheng],
Hu, J.Y.[Jing-Yu],
Enhancing video anomaly detection with learnable memory network:
A new approach to memory-based auto-encoders,
CVIU(241), 2024, pp. 103946.
Elsevier DOI
2403
Video anomaly detection, Unsupervised learning, Memory network, Transformer
BibRef
Luo, Y.[Ying],
Kang, G.L.[Guo-Liang],
Liu, K.[Kexin],
Zhuang, F.Z.[Fu-Zhen],
Lu, J.[Jinhu],
Taking a Closer Look at Factor Disentanglement:
Dual-Path Variational Autoencoder Learning for Domain Generalization,
MultMed(26), 2024, pp. 5872-5883.
IEEE DOI
2404
Semantics, Representation learning, Training, Task analysis,
Predictive models, Image reconstruction, Decoding, deep neural networks
BibRef
Zhou, X.[Xin],
Miao, C.Y.[Chun-Yan],
Disentangled Graph Variational Auto-Encoder for Multimodal
Recommendation With Interpretability,
MultMed(26), 2024, pp. 7543-7554.
IEEE DOI
2405
Numerical models, Vectors, Visualization, Image reconstruction,
Transformers, Semantics, Mutual information,
interpretability
BibRef
Tang, Q.Y.[Qian-Ying],
Wei, X.[Xiang],
Su, Q.[Qi],
Zhang, S.L.[Shun-Li],
ABAE: Auxiliary Balanced AutoEncoder for class-imbalanced
semi-supervised learning,
PRL(182), 2024, pp. 118-124.
Elsevier DOI
2405
Class-imbalanced learning, Semi-supervised learning,
Autoencoder, Label propagation
BibRef
Baykal, G.[Gulcin],
Kandemir, M.[Melih],
Unal, G.[Gozde],
EdVAE: Mitigating codebook collapse with evidential discrete
variational autoencoders,
PR(156), 2024, pp. 110792.
Elsevier DOI Code:
WWW Link.
2408
Vector quantized variational autoencoders,
Discrete variational autoencoders, Evidential deep learning, Codebook collapse
BibRef
Luo, H.[Hui],
Liu, X.[Xin],
Sun, J.[Jian],
Zhang, Y.[Yang],
Quaternion Vector Quantized Variational Autoencoder,
SPLetters(32), 2025, pp. 151-155.
IEEE DOI
2501
Quaternions, Vectors, Image reconstruction, Convolution,
Neural networks, Face recognition, Quantization (signal), Decoding,
vector quantized variational autoencoder
BibRef
Jin, X.[Xin],
Li, B.[Bohan],
Xie, B.[Baao],
Zhang, W.[Wenyao],
Liu, J.M.[Jin-Ming],
Li, Z.Q.[Zi-Qiang],
Yang, T.[Tao],
Zeng, W.J.[Wen-Jun],
Closed-loop Unsupervised Representation Disentanglement with Beta-VAE
Distillation and Diffusion Probabilistic Feedback,
ECCV24(XLV: 270-289).
Springer DOI
2412
BibRef
Luo, Y.H.[Yi-Hong],
Qiu, S.[Siya],
Tao, X.J.[Xing-Jian],
Cai, Y.J.[Yu-Jun],
Tang, J.[Jing],
Energy-calibrated VAE with Test Time Free Lunch,
ECCV24(LXXXV: 326-344).
Springer DOI
2412
BibRef
Kotwal, K.[Ketan],
Deshmukh, T.[Tanay],
Gopal, P.[Preeti],
Latent Enhancing Autoencoder for Occluded Image Classification,
ICIP24(894-900)
IEEE DOI
2411
Training, Accuracy, Head, Data models, Image reconstruction, Standards,
Image classification, Image Classification, Occlusion Handling,
Latent Enhancement
BibRef
Rivera, M.[Mariano],
How to Train Your VAE,
ICIP24(3882-3888)
IEEE DOI
2411
Training, Representation learning, Measurement, Histograms,
Posterior probability, Computer architecture, Decoding, GAN-VAE,
Gaussian Mixture
BibRef
Zhang, Y.[Ying],
Park, H.[Hyunhee],
Jia, H.[Hanchao],
Wang, F.[Fan],
Zhang, J.X.[Jian-Xing],
Kong, X.Y.[Xiang-Yu],
Adaptively Hierarchical Quantization Variational Autoencoder Based on
Feature Decoupling and Semantic Consistency for Image Generation,
ICIP24(2417-2423)
IEEE DOI
2411
Quantization (signal), Codes, Image coding, Image synthesis,
Semantics, Vectors, Image generation, VQ-VAE, Feature decoupling,
Sparse coding
BibRef
An, R.[Ruyi],
Li, Y.[Yewen],
He, X.[Xu],
Gu, P.J.[Peng-Jie],
Zhao, M.C.[Meng-Chen],
Li, D.[Dong],
Hao, J.[Jianye],
Wang, C.J.[Chao-Jie],
An, B.[Bo],
Zhou, M.Y.[Ming-Yuan],
Improving Unsupervised Hierarchical Representation With Reinforcement
Learning,
CVPR24(22946-22956)
IEEE DOI
2410
Training, Reinforcement learning,
Optimization, Information theory, unsupervised learning,
hierarchical variational autoencoder
BibRef
Huang, L.[Lun],
Qiu, Q.[Qiang],
Sapiro, G.[Guillermo],
PQ-VAE: Learning Hierarchical Discrete Representations with
Progressive Quantization,
GCV24(7550-7558)
IEEE DOI
2410
Representation learning, Codes, Image coding,
Quantization (signal), Semantics, Redundancy, Data compression,
Generative Models
BibRef
Zheng, D.[Dihan],
Zou, Y.H.[Yi-Hang],
Zhang, X.W.[Xiao-Wen],
Bao, C.L.[Cheng-Long],
SeNM-VAE: Semi-Supervised Noise Modeling with Hierarchical
Variational Autoencoder,
CVPR24(25889-25899)
IEEE DOI
2410
Training, Computational modeling, Noise, Superresolution,
Noise reduction, Training data, Linear programming
BibRef
Mazumder, A.[Alokendu],
Baruah, T.[Tirthajit],
Kumar, B.[Bhartendu],
Sharma, R.[Rishab],
Pattanaik, V.[Vishwajeet],
Rathore, P.[Punit],
Learning Low-Rank Latent Spaces with Simple Deterministic
Autoencoder: Theoretical and Empirical Insights,
WACV24(2839-2848)
IEEE DOI
2404
Representation learning, Adaptation models, Image synthesis,
Reliability theory, Task analysis, Covariance matrices, Algorithms
BibRef
Rossigneux, B.[Baptiste],
Kucher, I.[Inna],
Lorrain, V.[Vincent],
Casseau, E.[Emmanuel],
Surround the Nonlinearity: Inserting Foldable Convolutional
Autoencoders to Reduce Activation Footprint,
REDLCV23(1399-1403)
IEEE DOI
2401
BibRef
Xiang, W.[Weilai],
Yang, H.Y.[Hong-Yu],
Huang, D.[Di],
Wang, Y.H.[Yun-Hong],
Denoising Diffusion Autoencoders are Unified Self-supervised Learners,
ICCV23(15756-15766)
IEEE DOI
2401
BibRef
Bifis, A.[Aristeidis],
Psarakis, E.Z.[Emmanouil Z.],
Kosmopoulos, D.[Dimitrios],
Developing Robust and Lightweight Adversarial Defenders by Enforcing
Orthogonality on Attack-Agnostic Denoising Autoencoders,
REDLCV23(1264-1273)
IEEE DOI
2401
BibRef
Mao, Y.X.[Yu-Xin],
Zhang, J.[Jing],
Xiang, M.[Mochu],
Zhong, Y.[Yiran],
Dai, Y.C.[Yu-Chao],
Multimodal Variational Auto-encoder based Audio-Visual Segmentation,
ICCV23(954-965)
IEEE DOI
2401
BibRef
Prost, J.[Jean],
Houdard, A.[Antoine],
Almansa, A.[Andrés],
Papadakis, N.[Nicolas],
Inverse problem regularization with hierarchical variational
autoencoders,
ICCV23(22837-22848)
IEEE DOI Code:
WWW Link.
2401
BibRef
Nicodemou, V.C.[Vassilis C.],
Oikonomidis, I.[Iason],
Argyros, A.[Antonis],
RV-VAE: Integrating Random Variable Algebra into Variational
Autoencoders,
VIPriors23(196-205)
IEEE DOI Code:
WWW Link.
2401
BibRef
Coutinho, P.C.C.C.C.[Pedro C. C. C. C.],
Berthoumieu, Y.[Yannick],
Donias, M.[Marc],
Guillon, S.[Sébastien],
Weakly Supervised Disentanglement with Triplet Network,
ICIP23(2375-2379)
IEEE DOI
2312
BibRef
Girdhar, R.[Rohit],
El-Nouby, A.[Alaaeldin],
Singh, M.[Mannat],
Alwala, K.V.[Kalyan Vasudev],
Joulin, A.[Armand],
Misra, I.[Ishan],
OmniMAE: Single Model Masked Pretraining on Images and Videos,
CVPR23(10406-10417)
IEEE DOI
2309
BibRef
Tian, X.Y.[Xiao-Yu],
Ran, H.X.[Hao-Xi],
Wang, Y.[Yue],
Zhao, H.[Hang],
GeoMAE: Masked Geometric Target Prediction for Self-supervised Point
Cloud Pre-Training,
CVPR23(13570-13580)
IEEE DOI
2309
BibRef
Woo, S.[Sanghyun],
Debnath, S.[Shoubhik],
Hu, R.H.[Rong-Hang],
Chen, X.L.[Xin-Lei],
Liu, Z.[Zhuang],
Kweon, I.S.[In So],
Xie, S.[Saining],
ConvNeXt V2: Co-designing and Scaling ConvNets with Masked
Autoencoders,
CVPR23(16133-16142)
IEEE DOI
2309
BibRef
Chen, K.[Kai],
Liu, Z.[Zhili],
Hong, L.Q.[Lan-Qing],
Xu, H.[Hang],
Li, Z.G.[Zhen-Guo],
Yeung, D.Y.[Dit-Yan],
Mixed Autoencoder for Self-Supervised Visual Representation Learning,
CVPR23(22742-22751)
IEEE DOI
2309
BibRef
Wu, A.[Aming],
Deng, C.[Cheng],
Discriminating Known from Unknown Objects via Structure-Enhanced
Recurrent Variational AutoEncoder,
CVPR23(23956-23965)
IEEE DOI
2309
BibRef
Bui, T.[Tu],
Agarwal, S.[Shruti],
Yu, N.[Ning],
Collomosse, J.[John],
RoSteALS: Robust Steganography using Autoencoder Latent Space,
WMF23(933-942)
IEEE DOI
2309
BibRef
Zhu, J.G.[Jia-Geng],
Xie, H.[Hanchen],
Abd-Almageed, W.[Wael],
SW-VAE: Weakly Supervised Learn Disentangled Representation via Latent
Factor Swapping,
LLID22(73-87).
Springer DOI
2304
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
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
Yamazaki, M.[Meguru],
Kora, Y.[Yuichiro],
Nakao, T.[Takanori],
Lei, X.Y.[Xu-Ying],
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
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
Karunratanakul, K.[Korrawe],
Preechakul, K.[Konpat],
Aksan, E.[Emre],
Beeler, T.[Thabo],
Suwajanakorn, S.[Supasorn],
Tang, S.[Siyu],
Optimizing Diffusion Noise Can Serve As Universal Motion Priors,
CVPR24(1334-1345)
IEEE DOI
2410
motion denoising and completion.
Training, Computational modeling, Noise, Noise reduction,
Diffusion models, Trajectory, motion generation, diffusion model,
conditional generation
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, 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
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
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
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,
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
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
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PatchVAE: Learning Local Latent Codes for Recognition,
CVPR20(4745-4754)
IEEE DOI
2008
Task analysis, Data models, Image reconstruction, Visualization,
Standards, Decoding
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Vowels, M.J.,
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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
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Yang, L.,
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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
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Ding, Z.[Zheng],
Xu, Y.F.[Yi-Fan],
Xu, W.J.[Wei-Jian],
Parmar, G.[Gaurav],
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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
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Zheng, Z.L.[Zhi-Lin],
Sun, L.[Li],
Disentangling Latent Space for VAE by Label Relevant/Irrelevant
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CVPR19(12184-12193).
IEEE DOI
2002
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Variational Autoencoders Pursue PCA Directions (by Accident),
CVPR19(12398-12407).
IEEE DOI
2002
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Felsen, P.[Panna],
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Where Will They Go? Predicting Fine-Grained Adversarial Multi-agent
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ECCV18(XI: 761-776).
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1810
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Shang, W.L.[Wen-Ling],
Sohn, K.[Kihyuk],
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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
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Bidart, R.[Rene],
Wong, A.[Alexander],
Affine Variational Autoencoders,
ICIAR19(I:461-472).
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1909
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Mohbat,
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Dimensionality Reduction Using Discriminative Autoencoders for Remote
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1909
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Kim, M.Y.[Min-Young],
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Bayes-Factor-VAE: Hierarchical Bayesian Deep Auto-Encoder Models for
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ICCV19(2979-2987)
IEEE DOI
2004
Bayes methods, Gaussian processes, inference mechanisms,
learning (artificial intelligence), latent dimensions,
Maximum likelihood estimation
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Tan, Q.,
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Variational Autoencoders for Deforming 3D Mesh Models,
CVPR18(5841-5850)
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1812
Shape, Solid modeling, Deformable models, Analytical models, Geometry
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Yoo, Y.,
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Demiris, Y.,
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Variational Autoencoded Regression:
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CVPR17(2943-2952)
IEEE DOI
1711
Data models, Decoding, Gaussian processes, Image reconstruction,
Image sequences, Kernel, Visualization
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Abbasnejad, M.E.,
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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
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Goyal, P.,
Hu, Z.,
Liang, X.,
Wang, C.,
Xing, E.P.,
Mellon, C.,
Nonparametric Variational Auto-Encoders for Hierarchical
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ICCV17(5104-5112)
IEEE DOI
1802
Bayes methods, image representation, inference mechanisms,
learning (artificial intelligence), neural nets,
Standards
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Wang, W.[Wei],
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Wang, Y.Z.[Yi-Zhou],
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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 .