14.5.10.9.1 Training of Adversarial Networks

Chapter Contents (Back)
Adversarial Networks. Training. GAN.

Liu, X.L.[Xia-Lei], van de Weijer, J.[Joost], Bagdanov, A.D.[Andrew D.],
Exploiting Unlabeled Data in CNNs by Self-Supervised Learning to Rank,
PAMI(41), No. 8, August 2019, pp. 1862-1878.
IEEE DOI 1907
Task analysis, Training, Image quality, Visualization, Uncertainty, Labeling, Neural networks, Learning from rankings, active learning BibRef

Miyato, T.[Takeru], Maeda, S.I.[Shin-Ichi], Koyama, M.[Masanori], Ishii, S.[Shin],
Virtual Adversarial Training: A Regularization Method for Supervised and Semi-Supervised Learning,
PAMI(41), No. 8, August 2019, pp. 1979-1993.
IEEE DOI 1907
Training, Perturbation methods, Artificial neural networks, Semisupervised learning, Data models, Computational modeling, deep learning BibRef

Mopuri, K.R.[Konda Reddy], Ganeshan, A.[Aditya], Babu, R.V.[R. Venkatesh],
Generalizable Data-Free Objective for Crafting Universal Adversarial Perturbations,
PAMI(41), No. 10, October 2019, pp. 2452-2465.
IEEE DOI 1909
Perturbation methods, Task analysis, Data models, Training data, Feature extraction, Image segmentation, Machine learning, adversarial noise BibRef

Xie, J.W.[Jian-Wen], Lu, Y.[Yang], Gao, R.Q.[Rui-Qi], Zhu, S.C.[Song-Chun], Wu, Y.N.[Ying Nian],
Cooperative Training of Descriptor and Generator Networks,
PAMI(42), No. 1, January 2020, pp. 27-45.
IEEE DOI 1912
Generators, Training, Computational modeling, Inference algorithms, Heuristic algorithms, Analytical models, Deep generative models, MCMC teaching BibRef

Yu, Y., Li, X., Liu, F.,
Attention GANs: Unsupervised Deep Feature Learning for Aerial Scene Classification,
GeoRS(58), No. 1, January 2020, pp. 519-531.
IEEE DOI 2001
Learning systems, Feature extraction, Generators, Task analysis, Remote sensing, Generative adversarial networks, unsupervised deep feature learning BibRef

Mutlu, U.[Uras], Alpaydin, E.[Ethem],
Training bidirectional generative adversarial networks with hints,
PR(103), 2020, pp. 107320.
Elsevier DOI 2005
Generative Modeling, Generative Adversarial Networks, Unsupervised Learning, Autoencoders, Neural Networks, Deep Learning BibRef

Saito, M.[Masaki], Saito, S.[Shunta], Koyama, M.[Masanori], Kobayashi, S.[Sosuke],
Train Sparsely, Generate Densely: Memory-Efficient Unsupervised Training of High-Resolution Temporal GAN,
IJCV(128), No. 10-11, November 2020, pp. 2586-2606.
Springer DOI 2009
BibRef

Saito, M., Matsumoto, E., Saito, S.,
Temporal Generative Adversarial Nets with Singular Value Clipping,
ICCV17(2849-2858)
IEEE DOI 1802
Bayes methods, deconvolution, learning (artificial intelligence), unsupervised learning, video signal processing, generative model, Videos BibRef

Wang, X.J.[Xiao-Jie], Zhang, R.[Rui], Sun, Y.[Yu], Qi, J.Z.[Jian-Zhong],
Adversarial Distillation for Learning with Privileged Provisions,
PAMI(43), No. 3, March 2021, pp. 786-797.
IEEE DOI 2102
Training, Task analysis, Generators, privileged information, Computational modeling, Games, Lakes, Adversarial distillation BibRef

Zou, Z., Shi, T., Shi, Z., Ye, J.,
Adversarial Training for Solving Inverse Problems in Image Processing,
IP(30), 2021, pp. 2513-2525.
IEEE DOI 2102
Training, Inverse problems, Degradation, Image processing, Task analysis, Linear programming, Image denoising, bidirectional mapping BibRef

Qian, X., Cheng, X., Cheng, G., Yao, X., Jiang, L.,
Two-Stream Encoder GAN With Progressive Training for Co-Saliency Detection,
SPLetters(28), 2021, pp. 180-184.
IEEE DOI 2102
Feature extraction, Semantics, Training, Generators, Decoding, Generative adversarial networks, progressive training BibRef

Li, Y.J.[Yi-Jie], Liang, Q.K.[Qiao-Kang], Li, Z.W.[Zheng-Wei], Lei, Y.C.[You-Cheng], Sun, W.[Wei], Wang, Y.N.[Yao-Nan], Zhang, D.[Dan],
EdgeGAN: One-way mapping generative adversarial network based on the edge information for unpaired training set,
JVCIR(78), 2021, pp. 103187.
Elsevier DOI 2107
Lightweight generative adversarial network, Image conversion, Image-to-image translation, Unpaired image-to-image translation BibRef

Kobyzev, I.[Ivan], Prince, S.J.D.[Simon J.D.], Brubaker, M.A.[Marcus A.],
Normalizing Flows: An Introduction and Review of Current Methods,
PAMI(43), No. 11, November 2021, pp. 3964-3979.
IEEE DOI 2110
Estimation, Jacobian matrices, Mathematical model, Training, Computational modeling, Context modeling, Random variables, invertible neural networks BibRef

Park, S.W.[Sung Woo], Kwon, J.[Junseok],
SphereGAN: Sphere Generative Adversarial Network Based on Geometric Moment Matching and its Applications,
PAMI(44), No. 3, March 2022, pp. 1566-1580.
IEEE DOI 2202
BibRef
Earlier:
Sphere Generative Adversarial Network Based on Geometric Moment Matching,
CVPR19(4287-4296).
IEEE DOI 2002
Training, Linear programming, Manifolds, Generative adversarial networks, Measurement, geometric moment matching BibRef

Zhang, Z.Y.[Zhao-Yu], Li, M.Y.[Meng-Yan], Xie, H.N.[Hao-Nian], Yu, J.[Jun], Liu, T.L.[Tong-Liang], Chen, C.W.[Chang Wen],
TWGAN: Twin Discriminator Generative Adversarial Networks,
MultMed(24), 2022, pp. 677-688.
IEEE DOI 2202
Generative adversarial networks, Generators, Training, Optimization, Streaming media, Games, GAN, non-saturating loss, training instability BibRef

Guo, T.Y.[Tian-Yu], Xu, C.[Chang], Shi, B.X.[Bo-Xin], Xu, C.[Chao], Tao, D.C.[Da-Cheng],
Optimizing Latent Distributions for Non-Adversarial Generative Networks,
PAMI(44), No. 5, May 2022, pp. 2657-2672.
IEEE DOI 2204
Training, Generators, Optimization, Image reconstruction, Linear programming, Generative adversarial networks, distribution optimization BibRef

Jia, X.J.[Xiao-Jun], Zhang, Y.[Yong], Wu, B.Y.[Bao-Yuan], Wang, J.[Jue], Cao, X.C.[Xiao-Chun],
Boosting Fast Adversarial Training With Learnable Adversarial Initialization,
IP(31), 2022, pp. 4417-4430.
IEEE DOI 2207
Robustness, Training, Perturbation methods, Computational efficiency, Neural networks, Minimization, gradient information BibRef

Jia, X.J.[Xiao-Jun], Zhang, Y.[Yong], Wei, X.X.[Xing-Xing], Wu, B.Y.[Bao-Yuan], Ma, K.[Ke], Wang, J.[Jue], Cao, X.C.[Xiao-Chun],
Prior-Guided Adversarial Initialization for Fast Adversarial Training,
ECCV22(IV:567-584).
Springer DOI 2211
BibRef

Liu, X.F.[Xiao-Feng], Yang, C.[Chao], You, J.[Jane], Kuo, C.C.J.[C.C. Jay], Vijaya Kumar, B.V.K.,
Mutual Information Regularized Feature-Level Frankenstein for Discriminative Recognition,
PAMI(44), No. 9, September 2022, pp. 5243-5260.
IEEE DOI 2208
Task analysis, Semantics, Face recognition, Lighting, Mutual information, Training, Image color analysis, adversarial learning BibRef

Liu, X.F.[Xiao-Feng], Li, S.[Site], Kong, L.S.[Ling-Sheng], Xie, W.Q.[Wan-Qing], Jia, P.[Ping], You, J.[Jane], Kumar, B.V.K.,
Feature-Level Frankenstein: Eliminating Variations for Discriminative Recognition,
CVPR19(637-646).
IEEE DOI 2002
BibRef

Qian, Z.[Zhuang], Huang, K.[Kaizhu], Wang, Q.F.[Qiu-Feng], Zhang, X.Y.[Xu-Yao],
A survey of robust adversarial training in pattern recognition: Fundamental, theory, and methodologies,
PR(131), 2022, pp. 108889.
Elsevier DOI 2208
Survey, GAN Training. Adversarial examples, Adversarial training, Robust learning BibRef

Zhou, P.[Peng], Xie, L.X.[Ling-Xi], Ni, B.B.[Bing-Bing], Tian, Q.[Qi],
Searching Towards Class-Aware Generators for Conditional Generative Adversarial Networks,
SPLetters(29), 2022, pp. 1669-1673.
IEEE DOI 2208
Generators, Training, Microprocessors, Training data, Optimization, Convolution, class-aware BibRef

Zhang, W.L.[Wen-Long], Liu, Y.H.[Yi-Hao], Dong, C.[Chao], Qiao, Y.[Yu],
RankSRGAN: Super Resolution Generative Adversarial Networks With Learning to Rank,
PAMI(44), No. 10, October 2022, pp. 7149-7166.
IEEE DOI 2209
BibRef
Earlier:
RankSRGAN: Generative Adversarial Networks With Ranker for Image Super-Resolution,
ICCV19(3096-3105)
IEEE DOI 2004

WWW Link. Measurement, Generative adversarial networks, Generators, Visualization, Superresolution, Feature extraction, Training, learning to rank. image resolution, neural nets, unsupervised learning, RankSRGAN, Ranker, single image super-resolution, visual quality, Image quality BibRef

Wang, X.T.[Xin-Tao], Yu, K.[Ke], Wu, S.X.[Shi-Xiang], Gu, J.J.[Jin-Jin], Liu, Y.H.[Yi-Hao], Dong, C.[Chao], Qiao, Y.[Yu], Loy, C.C.[Chen Change],
ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks,
PerceptualRest18(V:63-79).
Springer DOI 1905
BibRef

Li, M.[Muyang], Lin, J.[Ji], Ding, Y.Y.[Yao-Yao], Liu, Z.J.[Zhi-Jian], Zhu, J.Y.[Jun-Yan], Han, S.[Song],
GAN Compression: Efficient Architectures for Interactive Conditional GANs,
PAMI(44), No. 12, December 2022, pp. 9331-9346.
IEEE DOI 2212
BibRef
Earlier: CVPR20(5283-5293)
IEEE DOI 2008
Training, Computational modeling, Generative adversarial networks, Computer architecture, neural architecture search. Generators, Image coding BibRef


Kumar, S.[Satyadwyoom], Narayan, A.[Apurva],
Introducing Diversity In Feature Scatter Adversarial Training Via Synthesis,
ICPR22(3069-3075)
IEEE DOI 2212
Training, Manifolds, Fuses, Perturbation methods, Focusing, Predictive models, Generators BibRef

Hu, Y.H.[Yu-Huang], Liu, S.C.[Shih-Chii],
Kernel Modulation: A Parameter-Efficient Method for Training Convolutional Neural Networks,
ICPR22(2192-2198)
IEEE DOI 2212
Training, Performance evaluation, Adaptation models, Transfer learning, Neural networks, Modulation BibRef

Wang, W.[Wei], Wu, Z.[Ziwen], Xiang, X.[Xueshuang], Li, Y.[Yue],
LDGAN: Latent Determined Ensemble Helps Removing IID Data Assumption and Cross-Node Sampling in Distributed GANs,
ICPR22(2135-2141)
IEEE DOI 2212
Weight measurement, Training, Aggregates, Distributed databases, Training data, Generative adversarial networks, Generators BibRef

Jeanneret, G.[Guillaume], Pérez, J.C.[Juan C.], Arbeláez, P.[Pablo],
A Hierarchical Assessment of Adversarial Severity,
AROW21(61-70)
IEEE DOI 2112
Training, Semantics, Neural networks, Benchmark testing, Extraterrestrial measurements BibRef

Cho, S.[Seungju], Byun, J.[Junyoung], Kwon, M.J.[Myung-Joon], Kim, Y.[Yoonji], Kim, C.[Changick],
Adversarial Training with Channel Attention Regularization,
ICIP22(2996-3000)
IEEE DOI 2211
Training, Deep learning, Sensitivity, Codes, Perturbation methods, Neural networks, Adversarial machine learning, Feature regularization BibRef

Ishikawa, T.[Tetsuya], Stent, S.[Simon],
Boosting Supervised Learning in Small Data Regimes with Conditional GAN Augmentation,
ICIP22(1351-1355)
IEEE DOI 2211
Training, Deep learning, Computational modeling, Neural networks, Training data, Conditional GAN, small data, augmentation, sample efficiency BibRef

Wei, J.H.[Jia-Heng], Liu, M.H.[Ming-Hao], Luo, J.H.[Jia-Hao], Zhu, A.[Andrew], Davis, J.[James], Liu, Y.[Yang],
DuelGAN: A Duel Between Two Discriminators Stabilizes the GAN Training,
ECCV22(XXIII:290-317).
Springer DOI 2211
BibRef

Xiao, J.[Jiayu], Li, L.[Liang], Wang, C.F.[Chao-Fei], Zha, Z.J.[Zheng-Jun], Huang, Q.M.[Qing-Ming],
Few Shot Generative Model Adaption via Relaxed Spatial Structural Alignment,
CVPR22(11194-11203)
IEEE DOI 2210
GAN Training. Training, Adaptation models, Image coding, Correlation, Codes, Computational modeling, Transfer/low-shot/long-tail learning BibRef

Wang, J.Y.[Jian-Yuan], Yang, C.[Ceyuan], Xu, Y.H.[Ying-Hao], Shen, Y.J.[Yu-Jun], Li, H.D.[Hong-Dong], Zhou, B.[Bolei],
Improving GAN Equilibrium by Raising Spatial Awareness,
CVPR22(11275-11283)
IEEE DOI 2210
Training, Heating systems, Visualization, Image synthesis, Computational modeling, Games, Image and video synthesis and generation BibRef

Parmar, G.[Gaurav], Zhang, R.[Richard], Zhu, J.Y.[Jun-Yan],
On Aliased Resizing and Surprising Subtleties in GAN Evaluation,
CVPR22(11400-11410)
IEEE DOI 2210
Measurement, Training, Image coding, Computational modeling, Pipelines, Transform coding, Signal processing, Vision + graphics BibRef

Aung, A.P.P.[Aye Phyu Phyu], Wang, X.[Xinrun], Yu, R.S.[Run-Sheng], An, B.[Bo], Jayavelu, S.[Senthilnath], Li, X.L.[Xiao-Li],
DO-GAN: A Double Oracle Framework for Generative Adversarial Networks,
CVPR22(11265-11274)
IEEE DOI 2210
Measurement, Training, Scalability, Optimization methods, Games, Generative adversarial networks BibRef

Wang, L.[Lan], Boddeti, V.N.[Vishnu Naresh],
Do learned representations respect causal relationships?,
CVPR22(264-274)
IEEE DOI 2210
Training, Representation learning, Shape, Semantics, Process control, Prediction methods, Machine learning, Datasets and evaluation, Explainable computer vision BibRef

Yamaguchi, S.[Shin'ya], Kanai, S.[Sekitoshi],
F-Drop &Match: GANs with a Dead Zone in the High-Frequency Domain,
ICCV21(6723-6731)
IEEE DOI 2203
Training, Matched filters, Sensitivity, Frequency-domain analysis, Perturbation methods, Fitting, Linear programming, Image and video synthesis BibRef

Feng, Q.L.[Qian-Li], Guo, C.Q.[Chen-Qi], Benitez-Quiroz, F.[Fabian], Martinez, A.[Aleix],
When do GANs replicate? On the choice of dataset size,
ICCV21(6681-6690)
IEEE DOI 2203
Training, Image quality, Training data, Estimation, Market research, Complexity theory, Neural generative models, Image and video synthesis BibRef

He, Z.L.[Zhen-Liang], Kan, M.[Meina], Shan, S.G.[Shi-Guang],
EigenGAN: Layer-Wise Eigen-Learning for GANs,
ICCV21(14388-14397)
IEEE DOI 2203
Training, Codes, Image color analysis, Semantics, Generative adversarial networks, Generators, Representation learning BibRef

Cui, J.[Jiequan], Liu, S.[Shu], Wang, L.W.[Li-Wei], Jia, J.Y.[Jia-Ya],
Learnable Boundary Guided Adversarial Training,
ICCV21(15701-15710)
IEEE DOI 2203
Training, Degradation, Codes, Computational modeling, Benchmark testing, Data models, Adversarial learning, Recognition and classification BibRef

Wu, Y.L.[Yi-Lun], Shuai, H.H.[Hong-Han], Tam, Z.R.[Zhi-Rui], Chiu, H.Y.[Hong-Yu],
Gradient Normalization for Generative Adversarial Networks,
ICCV21(6353-6362)
IEEE DOI 2203
Training, Generative adversarial networks, Neural generative models, BibRef

Shoshan, A.[Alon], Bhonker, N.[Nadav], Kviatkovsky, I.[Igor], Medioni, G.[Gérard],
GAN-Control: Explicitly Controllable GANs,
ICCV21(14063-14073)
IEEE DOI 2203
Training, Hair, Solid modeling, Image synthesis, Image color analysis, Lighting, Image and video synthesis, Faces, Neural generative models BibRef

Wang, S.Y.[Sheng-Yu], Bau, D.[David], Zhu, J.Y.[Jun-Yan],
Sketch Your Own GAN,
ICCV21(14030-14040)
IEEE DOI 2203
Training, Image quality, Deep learning, Visualization, Interpolation, Shape, Image and video synthesis, Neural generative models BibRef

Zhou, P.[Peng], Xie, L.X.[Ling-Xi], Ni, B.B.[Bing-Bing], Geng, C.[Cong], Tian, Q.[Qi],
Omni-GAN: On the Secrets of cGANs and Beyond,
ICCV21(14041-14051)
IEEE DOI 2203
Training, Image resolution, Image synthesis, Computational modeling, Performance gain, BibRef

Kong, S.[Shu], Ramanan, D.[Deva],
OpenGAN: Open-Set Recognition via Open Data Generation,
ICCV21(793-802)
IEEE DOI 2203
Award, Marr Prize, HM. Training, Image segmentation, Image recognition, Semantics, Training data, Machine learning, Generative adversarial networks, Adversarial learning BibRef

Singla, V.[Vasu], Singla, S.[Sahil], Feizi, S.[Soheil], Jacobs, D.[David],
Low Curvature Activations Reduce Overfitting in Adversarial Training,
ICCV21(16403-16413)
IEEE DOI 2203
Training, Computational modeling, Neural networks, Robustness, Standards, Adversarial learning, BibRef

Bhaskara, V.S.[Vineeth S.], Aumentado-Armstrong, T.[Tristan], Jepson, A.[Allan], Levinshtein, A.[Alex],
GraN-GAN: Piecewise Gradient Normalization for Generative Adversarial Networks,
WACV22(2432-2441)
IEEE DOI 2202
Training, Measurement, Image synthesis, Performance gain, Generative adversarial networks, Standards, GANs BibRef

Jakoel, K.[Karin], Efraim, L.[Liron], Shaham, T.R.[Tamar Rott],
GANs Spatial Control via Inference-Time Adaptive Normalization,
WACV22(31-40)
IEEE DOI 2202
Training, Visualization, Adaptation models, Process control, Generative adversarial networks, Task analysis, GANs BibRef

Ye, F.[Fei], Bors, A.G.[Adrian G.],
Lifelong Twin Generative Adversarial Networks,
ICIP21(1289-1293)
IEEE DOI 2201
Training, Knowledge engineering, Interpolation, Databases, Image processing, Generative adversarial networks, Teacher-Student learning models BibRef

Hwang, J.W.[Joong-Won], Lee, Y.[Youngwan], Oh, S.[Sungchan], Bae, Y.[Yuseok],
Adversarial Training With Stochastic Weight Average,
ICIP21(814-818)
IEEE DOI 2201
Training, Deep learning, Image processing, Stochastic processes, Robustness, Artificial intelligence, Adversarial training, Hard Example Mining BibRef

Modas, A.[Apostolos], Xompero, A.[Alessio], Sanchez-Matilla, R.[Ricardo], Frossard, P.[Pascal], Cavallaro, A.[Andrea],
Improving Filling Level Classification with Adversarial Training,
ICIP21(829-833)
IEEE DOI 2201
Training, Shape, Image processing, Transfer learning, Training data, Glass, Containers, Adversarial training, Transfer learning, Classification BibRef

Wang, J.Y.[Jin-Yu], Li, Y.[Yang], Yang, H.T.[Hai-Tao], Zheng, F.J.[Feng-Jie], Gao, Y.G.[Yu-Ge], Li, G.Y.[Gao-Yuan],
GAN Evaluation Method Based on Remote Sensing Image Information,
ICIVC21(295-300)
IEEE DOI 2112
Training, Visualization, Uncertainty, Stability criteria, Optimization methods, Generative adversarial networks, GAN BibRef

Chai, L.[Lucy], Zhu, J.Y.[Jun-Yan], Shechtman, E.[Eli], Isola, P.[Phillip], Zhang, R.[Richard],
Ensembling with Deep Generative Views,
CVPR21(14992-15002)
IEEE DOI 2111
Training, Codes, Sensitivity, Cats, Perturbation methods, Generators, Automobiles BibRef

Pan, T.[Tian], Song, Y.B.[Yi-Bing], Yang, T.[Tianyu], Jiang, W.H.[Wen-Hao], Liu, W.[Wei],
VideoMoCo: Contrastive Video Representation Learning with Temporally Adversarial Examples,
CVPR21(11200-11209)
IEEE DOI 2111
Training, Degradation, Adaptation models, Computational modeling, Video sequences, Image representation, Robustness BibRef

Lu, H.[Hao], Han, H.[Hu], Zhou, S.K.[S. Kevin],
Dual-GAN: Joint BVP and Noise Modeling for Remote Physiological Measurement,
CVPR21(12399-12408)
IEEE DOI 2111
Training, Solid modeling, Pulse measurements, Volume measurement, Predictive models, Adversarial machine learning, Noise measurement BibRef

Tseng, H.Y.[Hung-Yu], Jiang, L.[Lu], Liu, C.[Ce], Yang, M.H.[Ming-Hsuan], Yang, W.L.[Wei-Long],
Regularizing Generative Adversarial Networks under Limited Data,
CVPR21(7917-7927)
IEEE DOI 2111
Training, Codes, Computational modeling, Training data, Benchmark testing, Fasteners BibRef

Armandpour, M.[Mohammadreza], Sadeghian, A.[Ali], Li, C.Y.[Chun-Yuan], Zhou, M.[Mingyuan],
Partition-Guided GANs,
CVPR21(5095-5105)
IEEE DOI 2111
Training, Manifolds, Generative adversarial networks, Generators, Pattern recognition BibRef

Hendrycks, D.[Dan], Zhao, K.[Kevin], Basart, S.[Steven], Steinhardt, J.[Jacob], Song, D.[Dawn],
Natural Adversarial Examples,
CVPR21(15257-15266)
IEEE DOI 2111
Training, Filtration, Convolution, Computational modeling, Machine learning, Computer architecture BibRef

Xu, J.J.[Jian-Jin], Zheng, C.X.[Chang-Xi],
Linear Semantics in Generative Adversarial Networks,
CVPR21(9347-9356)
IEEE DOI 2111
Training, Image segmentation, Annotations, Face recognition, Semantics, Layout, Process control BibRef

Chen, P.C.[Pin-Chun], Kung, B.H.[Bo-Han], Chen, J.C.[Jun-Cheng],
Class-Aware Robust Adversarial Training for Object Detection,
CVPR21(10415-10424)
IEEE DOI 2111
Training, Perturbation methods, Computational modeling, Object detection, Robustness, Pattern recognition BibRef

Shen, C.C.[Cheng-Chao], Yin, Y.T.[You-Tan], Wang, X.C.[Xin-Chao], Li, X.B.[Xu-Bin], Song, J.[Jie], Song, M.L.[Ming-Li],
Training Generative Adversarial Networks in One Stage,
CVPR21(3349-3359)
IEEE DOI 2111
Training, Image synthesis, Network architecture, Generative adversarial networks, Solids, Generators BibRef

Wu, W.B.[Wei-Bin], Su, Y.X.[Yu-Xin], Lyu, M.R.[Michael R.], King, I.[Irwin],
Improving the Transferability of Adversarial Samples with Adversarial Transformations,
CVPR21(9020-9029)
IEEE DOI 2111
Training, Resistance, Deep learning, Computational modeling, Benchmark testing, Distortion BibRef

Ouyang, X.[Xu], Chen, Y.[Ying], Agam, G.[Gady],
Accelerated WGAN update strategy with loss change rate balancing,
WACV21(2545-2554)
IEEE DOI 2106
Wasserstein GAN. Training, Adaptive systems, Generative adversarial networks, Nash equilibrium, Generators BibRef

Wang, Z.[Zi],
Learning Fast Converging, Effective Conditional Generative Adversarial Networks with a Mirrored Auxiliary Classifier,
WACV21(2565-2574)
IEEE DOI 2106
Training, Image synthesis, Computational modeling, Transfer learning, Computer architecture BibRef

Zuo, Y.[Yan], Avraham, G.[Gil], Drummond, T.W.[Tom W.],
Improved Training of Generative Adversarial Networks Using Decision Forests,
WACV21(3491-3500)
IEEE DOI 2106
Training, Toy manufacturing industry, Neural networks, Performance gain, Generative adversarial networks BibRef

Kavalerov, I.[Ilya], Czaja, W.[Wojciech], Chellappa, R.[Rama],
A Multi-Class Hinge Loss for Conditional GANs,
WACV21(1289-1298)
IEEE DOI 2106
Training, Image quality, Fasteners, Generators, Classification algorithms BibRef

Hinz, T.[Tobias], Fisher, M.[Matthew], Wang, O.[Oliver], Wermter, S.[Stefan],
Improved Techniques for Training Single-Image GANs,
WACV21(1299-1308)
IEEE DOI 2106
Training, Image resolution, Image synthesis, Computational modeling, Animation BibRef

Collier, E.[Edward], Mukhopadhyay, S.[Supratik],
GAP: Quantifying the Generative Adversarial Set and Class Feature Applicability of Deep Neural Networks,
ICPR21(8384-8391)
IEEE DOI 2105
Training, Knowledge engineering, Neural networks, Focusing, Generative adversarial networks, Generators BibRef

Yang, X.L.[Xiu-Long], Ji, S.H.[Shi-Hao],
Learning with Multiplicative Perturbations,
ICPR21(1321-1328)
IEEE DOI 2105
Adversarial Training (AT) and Virtual Adversarial Training (VAT) of deep network. Training, Visualization, Additives, Perturbation methods, Neurons, Neural networks, Benchmark testing BibRef

Torfi, A.[Amirsina], Beyki, M.[Mohammadreza], Fox, E.A.[Edward A.],
On the Evaluation of Generative Adversarial Networks By Discriminative Models,
ICPR21(991-998)
IEEE DOI 2105
Measurement, Training, Visualization, Technological innovation, Neural networks, Estimation, Generative adversarial networks BibRef

Ahmetoglu, A.[Alper], Alpaydin, E.[Ethem],
Hierarchical Mixtures of Generators for Adversarial Learning,
ICPR21(316-323)
IEEE DOI 2105
Training, Neural networks, Transforms, Generative adversarial networks, Generators, Data models, Probability distribution BibRef

Marriott, R., Romdhani, S., Chen, L.,
Taking Control of Intra-class Variation in Conditional GANs Under Weak Supervision,
FG20(257-264)
IEEE DOI 2102
Lighting, Generators, Training, Semantics, Generative adversarial networks, Biometrics (access control) BibRef

Wan, W.T.[Wei-Tao], Chen, J.S.[Jian-Sheng], Yang, M.H.[Ming-Hsuan],
Adversarial Training with Bi-Directional Likelihood Regularization for Visual Classification,
ECCV20(XXIV:785-800).
Springer DOI 2012
BibRef

Yazici, Y., Foo, C.S., Winkler, S., Yap, K.H., Chandrasekhar, V.,
Empirical Analysis Of Overfitting And Mode Drop In GAN Training,
ICIP20(1651-1655)
IEEE DOI 2011
Training, Generators, Generative adversarial networks, Semantics, Noise measurement, Deep Learning BibRef

An, D.S.[Dong-Sheng], Guo, Y.[Yang], Zhang, M.[Min], Qi, X.[Xin], Lei, N.[Na], Gu, X.F.[Xian-Fang],
AE-OT-GAN: Training GANs from Data Specific Latent Distribution,
ECCV20(XXVI:548-564).
Springer DOI 2011
BibRef

Xiong, Y.H.[Yuan-Hao], Hsieh, C.J.[Cho-Jui],
Improved Adversarial Training via Learned Optimizer,
ECCV20(VIII:85-100).
Springer DOI 2011
BibRef

Qin, Y.P.[Yi-Peng], Mitra, N.[Niloy], Wonka, P.[Peter],
How Does Lipschitz Regularization Influence GAN Training?,
ECCV20(XVI: 310-326).
Springer DOI 2010
BibRef

Williams, F.[Francis], Parent-Lévesque, J.[Jérôme], Nowrouzezahrai, D.[Derek], Panozzo, D.[Daniele], Yi, K.M.[Kwang Moo], Tagliasacchi, A.[Andrea],
VoronoiNet: General Functional Approximators with Local Support,
L3DGM20(1069-1073)
IEEE DOI 2008
Shape, Decoding, Image reconstruction, Training, Task analysis BibRef

Xing, X.L.[Xiang-Lei], Wu, T.F.[Tian-Fu], Zhu, S.C.[Song-Chun], Wu, Y.N.[Ying Nian],
Inducing Hierarchical Compositional Model by Sparsifying Generator Network,
CVPR20(14284-14293)
IEEE DOI 2008
Generators, Image generation, Training, Image reconstruction, Image coding, Computational modeling BibRef

Liu, S., Wang, T., Bau, D., Zhu, J., Torralba, A.,
Diverse Image Generation via Self-Conditioned GANs,
CVPR20(14274-14283)
IEEE DOI 2008
Generators, Training, Clustering algorithms, Partitioning algorithms, Image generation, Computational modeling BibRef

Chen, J., Konrad, J., Ishwar, P.,
A Cyclically-Trained Adversarial Network for Invariant Representation Learning,
AML-CV20(3393-3402)
IEEE DOI 2008
Training, Generators, Neural networks, Task analysis, Image generation, Decoding BibRef

Gao, R.Q.[Rui-Qi], Nijkamp, E.[Erik], Kingma, D.P.[Diederik P.], Xu, Z.[Zhen], Dai, A.M.[Andrew M.], Wu, Y.N.[Ying Nian],
Flow Contrastive Estimation of Energy-Based Models,
CVPR20(7515-7525)
IEEE DOI 2008
Data models, Adaptation models, Maximum likelihood estimation, Computational modeling, Training BibRef

Lee, D., Park, H., Pham, T., Yoo, C.D.,
Learning Augmentation Network via Influence Functions,
CVPR20(10958-10967)
IEEE DOI 2008
Training, Computational modeling, Data models, Mathematical model, Generative adversarial networks, Generators, Neural networks BibRef

Liu, Y., Deng, G., Zeng, X., Wu, S., Yu, Z., Wong, H.,
Regularizing Discriminative Capability of CGANs for Semi-Supervised Generative Learning,
CVPR20(5719-5728)
IEEE DOI 2008
Training, Generators, Predictive models, Image generation, Data models, Games BibRef

Daras, G., Odena, A., Zhang, H., Dimakis, A.G.,
Your Local GAN: Designing Two Dimensional Local Attention Mechanisms for Generative Models,
CVPR20(14519-14527)
IEEE DOI 2008
Flow graphs, Training, Visualization, Head, Kernel, Generative adversarial networks BibRef

Zhou, R., Shen, Y.,
End-to-End Adversarial-Attention Network for Multi-Modal Clustering,
CVPR20(14607-14616)
IEEE DOI 2008
Clustering methods, Kernel, Training, Task analysis, Network architecture, Neural networks, Geometry BibRef

Guo, T., Xu, C., Huang, J., Wang, Y., Shi, B., Xu, C., Tao, D.,
On Positive-Unlabeled Classification in GAN,
CVPR20(8382-8390)
IEEE DOI 2008
Training, Generators, Generative adversarial networks, Linear programming, Standards, Games BibRef

Schönfeld, E., Schiele, B., Khoreva, A.,
A U-Net Based Discriminator for Generative Adversarial Networks,
CVPR20(8204-8213)
IEEE DOI 2008
Generators, Decoding, Training, Generative adversarial networks, Image segmentation, Computer architecture BibRef

Durall, R., Keuper, M., Keuper, J.,
Watch Your Up-Convolution: CNN Based Generative Deep Neural Networks Are Failing to Reproduce Spectral Distributions,
CVPR20(7887-7896)
IEEE DOI 2008
Convolution, Distortion, Neural networks, Training, Generative adversarial networks BibRef

Ansari, A.F.[A. Fatir], Scarlett, J., Soh, H.,
A Characteristic Function Approach to Deep Implicit Generative Modeling,
CVPR20(7476-7484)
IEEE DOI 2008
Generators, Measurement, Training, Generative adversarial networks, Optimization, Computational modeling BibRef

Karnewar, A., Wang, O.,
MSG-GAN: Multi-Scale Gradients for Generative Adversarial Networks,
CVPR20(7796-7805)
IEEE DOI 2008
Generators, Image resolution, Training, Image generation, Task analysis, Generative adversarial networks BibRef

Tao, S., Wang, J.,
Alleviation of Gradient Exploding in GANs: Fake Can Be Real,
CVPR20(1188-1197)
IEEE DOI 2008
Training, Generators, Generative adversarial networks, Interpolation, Neural networks, Gaussian distribution BibRef

Brodie, M., Rasmussen, B., Tensmeyer, C., Corbitt, S., Martinez, T.,
CoachGAN,
WACV20(3472-3481)
IEEE DOI 2006
Training, Generators, Integrated circuits, Generative adversarial networks, Optimization, Neural networks BibRef

Huang, R., Xu, W., Lee, T., Cherian, A., Wang, Y., Marks, T.K.,
FX-GAN: Self-Supervised GAN Learning via Feature Exchange,
WACV20(3183-3191)
IEEE DOI 2006
Task analysis, Generative adversarial networks, Generators, Training, Optimization, Games BibRef

Agustsson, E., Tschannen, M., Mentzer, F., Timofte, R., Van Gool, L.J.,
Generative Adversarial Networks for Extreme Learned Image Compression,
ICCV19(221-231)
IEEE DOI 2004
data compression, image classification, image coding, image colour analysis, learning (artificial intelligence), Training BibRef

Bau, D., Zhu, J., Wulff, J., Peebles, W., Zhou, B., Strobelt, H., Torralba, A.,
Seeing What a GAN Cannot Generate,
ICCV19(4501-4510)
IEEE DOI 2004
convolutional neural nets, data visualisation, image segmentation, object detection, object classes, GAN layer, Training BibRef

Jenni, S.[Simon], Favaro, P.[Paolo],
On Stabilizing Generative Adversarial Training With Noise,
CVPR19(12137-12145).
IEEE DOI 2002
BibRef

Horiuchi, Y.[Yusuke], Iizuka, S.[Satoshi], Simo-Serra, E.[Edgar], Ishikawa, H.[Hiroshi],
Spectral Normalization and Relativistic Adversarial Training for Conditional Pose Generation with Self-Attention,
MVA19(1-5)
DOI Link 1911
image resolution, learning (artificial intelligence), pose estimation, spectral normalization, Fading channels BibRef

Tong, X.Y.[Xin-Yi], Yin, J.H.[Ji-Hao], Han, B.N.[Bing-Nan], Qv, H.[Hui],
Few-Shot Learning With Attention-Weighted Graph Convolutional Networks For Hyperspectral Image Classification,
ICIP20(1686-1690)
IEEE DOI 2011
Information processing, Training, Remote sensing, Machine learning, Pattern recognition, Few-shot learning, attention mechanism BibRef

Yin, J.H.[Ji-Hao], Li, W.Y.[Wen-Yue], Han, B.N.[Bing-Nan],
Hyperspectral Image Classification Based on Generative Adversarial Network with Dropblock,
ICIP19(405-409)
IEEE DOI 1910
Hyperspectral classification, generative adversarial networks, spatial semantic information BibRef

You, Z., Ye, J., Li, K., Xu, Z., Wang, P.,
Adversarial Noise Layer: Regularize Neural Network by Adding Noise,
ICIP19(909-913)
IEEE DOI 1910
regularization, adversarial training, classification, convolutional neural network BibRef

Nguyen, N.M., Ray, N.,
Generative Adversarial Networks Using Adaptive Convolution,
CRV19(129-134)
IEEE DOI 1908
Convolution, Generators, Generative adversarial networks, Training, Adaptation models, Generative Adversarial Networks BibRef

Esser, P.[Patrick], Sutter, E.[Ekaterina],
A Variational U-Net for Conditional Appearance and Shape Generation,
CVPR18(8857-8866)
IEEE DOI 1812
Shape, Generators, Image generation, Standards, Image color analysis, Training, Footwear BibRef

Russo, P., Carlucci, F.M., Tommasi, T., Caputo, B.,
From Source to Target and Back: Symmetric Bi-Directional Adaptive GAN,
CVPR18(8099-8108)
IEEE DOI 1812
Generators, Training, Adaptation models, Image reconstruction, Bidirectional control, Image generation BibRef

Deshpande, I.[Ishan], Zhang, Z.Y.[Zi-Yu], Schwing, A.[Alexander],
Generative Modeling Using the Sliced Wasserstein Distance,
CVPR18(3483-3491)
IEEE DOI 1812
Training, Generators, Stability analysis, Optimization, Task analysis, Computational modeling BibRef

Song, Y., Ma, C., Wu, X., Gong, L., Bao, L., Zuo, W., Shen, C., Lau, R.W.H., Yang, M.,
VITAL: VIsual Tracking via Adversarial Learning,
CVPR18(8990-8999)
IEEE DOI 1812
Target tracking, Training, Feature extraction, Generators, Visualization, Entropy BibRef

Zhang, X., Wei, Y., Feng, J., Yang, Y., Huang, T.,
Adversarial Complementary Learning for Weakly Supervised Object Localization,
CVPR18(1325-1334)
IEEE DOI 1812
Training, Feature extraction, Head, Legged locomotion, Task analysis, Pattern recognition, Object recognition BibRef

Chou, Y., Chen, C., Liu, K., Chen, C.,
Stingray Detection of Aerial Images Using Augmented Training Images Generated by a Conditional Generative Model,
Environmental18(1484-14846)
IEEE DOI 1812
Training, Object detection, Generators, Sea surface, Generative adversarial networks, Detectors BibRef

Mattyus, G., Urtasun, R.,
Matching Adversarial Networks,
CVPR18(8024-8032)
IEEE DOI 1812
Generators, Training, Task analysis, Perturbation methods, Generative adversarial networks, Image segmentation BibRef

Gao, R., Lu, Y., Zhou, J., Zhu, S., Wu, Y.N.,
Learning Generative ConvNets via Multi-grid Modeling and Sampling,
CVPR18(9155-9164)
IEEE DOI 1812
Training, Monte Carlo methods, Data models, Maximum likelihood estimation, Energy resolution, Probabilistic logic BibRef

Zhang, Z., Yang, L., Zheng, Y.,
Translating and Segmenting Multimodal Medical Volumes with Cycle- and Shape-Consistency Generative Adversarial Network,
CVPR18(9242-9251)
IEEE DOI 1812
Image segmentation, Generators, Biomedical imaging, Task analysis, Computed tomography, Training BibRef

Chavdarova, T., Fleuret, F.,
SGAN: An Alternative Training of Generative Adversarial Networks,
CVPR18(9407-9415)
IEEE DOI 1812
Pattern recognition BibRef

Mopuri, K.R., Ojha, U., Garg, U., Babu, R.V.,
NAG: Network for Adversary Generation,
CVPR18(742-751)
IEEE DOI 1812
Perturbation methods, Generators, Generative adversarial networks, Training, Machine learning, Neural networks BibRef

Qi, G., Zhang, L., Hu, H., Edraki, M., Wang, J., Hua, X.,
Global Versus Localized Generative Adversarial Nets,
CVPR18(1517-1525)
IEEE DOI 1812
Manifolds, Generators, Geometry, Training, Data models, Semisupervised learning BibRef

Lee, K., Xu, W., Fan, F., Tu, Z.,
Wasserstein Introspective Neural Networks,
CVPR18(3702-3711)
IEEE DOI 1812
Generative adversarial networks, Training, Generators, Computational modeling, Convolutional neural networks BibRef

Poursaeed, O., Katsman, I., Gao, B., Belongie, S.,
Generative Adversarial Perturbations,
CVPR18(4422-4431)
IEEE DOI 1812
Perturbation methods, Generators, Task analysis, Semantics, Image segmentation, Iterative methods, Training BibRef

Shen, Y., Ji, R., Zhang, S., Zuo, W., Wang, Y.,
Generative Adversarial Learning Towards Fast Weakly Supervised Detection,
CVPR18(5764-5773)
IEEE DOI 1812
Detectors, Proposals, Generators, Training, Pipelines, Generative adversarial networks BibRef

Dizaji, K.G., Zheng, F., Nourabadi, N.S., Yang, Y., Deng, C., Huang, H.,
Unsupervised Deep Generative Adversarial Hashing Network,
CVPR18(3664-3673)
IEEE DOI 1812
Generators, Training, Task analysis, Generative adversarial networks, Binary codes BibRef

Cao, Y., Liu, B., Long, M., Wang, J.,
HashGAN: Deep Learning to Hash with Pair Conditional Wasserstein GAN,
CVPR18(1287-1296)
IEEE DOI 1812
Generators, Quantization (signal), Training, Training data, Generative adversarial networks BibRef

Zhang, C., Feng, Y., Qiang, B., Shang, J.,
Wasserstein Generative Recurrent Adversarial Networks for Image Generating,
ICPR18(242-247)
IEEE DOI 1812
Generators, Generative adversarial networks, Training, Mathematical model, Earth, Image generation, recurrent nerual netwoks BibRef

Yu, P., Song, K., Lu, J.,
Generating Adversarial Examples With Conditional Generative Adversarial Net,
ICPR18(676-681)
IEEE DOI 1812
Training, Perturbation methods, Generators, Data models, Generative adversarial networks, Computational modeling, BibRef

Sun, D., Zhang, Q., Yang, J.,
Pyramid Embedded Generative Adversarial Network for Automated Font Generation,
ICPR18(976-981)
IEEE DOI 1812
Generators, Decoding, Generative adversarial networks, Training, Task analysis, Image generation BibRef

Vivek, B.S., Mopuri, K.R.[Konda Reddy], Babu, R.V.[R. Venkatesh],
Gray-Box Adversarial Training,
ECCV18(XV: 213-228).
Springer DOI 1810
BibRef

Ge, H.[Hao], Xia, Y.[Yin], Chen, X.[Xu], Berry, R.[Randall], Wu, Y.[Ying],
Fictitious GAN: Training GANs with Historical Models,
ECCV18(I: 122-137).
Springer DOI 1810
BibRef

Sah, S.[Shagan], Shringi, A.[Ameya], Peri, D.[Dheeraj], Hamilton, J.[John], Savakis, A.[Andreas], Ptucha, R.[Ray],
Multimodal Reconstruction Using Vector Representation,
ICIP18(3763-3767)
IEEE DOI 1809
Image reconstruction, Training, Decoding, Visualization, Image generation, Task analysis, Correlation BibRef

Halici, E., Alatan, A.A.[A. Aydin],
Object Localization Without Bounding Box Information Using Generative Adversarial Reinforcement Learning,
ICIP18(3728-3732)
IEEE DOI 1809
Agriculture, Learning (artificial intelligence), Automobiles, Training, Image databases, Measurement, Object Localization, Generative Adversarial Reinforcement Learning BibRef

Chiaroni, F., Rahal, M., Hueber, N., Dufaux, F.,
Learning with A Generative Adversarial Network From a Positive Unlabeled Dataset for Image Classification,
ICIP18(1368-1372)
IEEE DOI 1809
Training, Generative adversarial networks, Learning systems, Computational modeling, Kernel, Neurons, Generative Models BibRef

Ravanbakhsh, M., Baydoun, M., Campo, D., Marin, P., Martin, D., Marcenaro, L., Regazzoni, C.S.,
Hierarchy of GANs for Learning Embodied Self-Awareness Model,
ICIP18(1987-1991)
IEEE DOI 1809
Data models, Optical imaging, Training, Task analysis, Generative adversarial networks, Anomaly detection BibRef

Kosmopoulos, D.I.,
A Prototype Towards Modeling Visual Data Using Decentralized Generative Adversarial Networks,
ICIP18(4163-4167)
IEEE DOI 1809
Training, Generative adversarial networks, Optimization, Generators, Data models, Games, decentralized learning, BibRef

Rukhkhattak, G., Vallecorsa, S., Carminati, F.,
Three Dimensional Energy Parametrized Generative Adversarial Networks for Electromagnetic Shower Simulation,
ICIP18(3913-3917)
IEEE DOI 1809
Generative adversarial networks, Detectors, Generators, Physics, Training, Monte Carlo methods, HEP, Simulation, GAN BibRef

Kancharla, P., Channappayya, S.S.,
Improving the Visual Quality of Generative Adversarial Network (GAN)-Generated Images Using the Multi-Scale Structural Similarity Index,
ICIP18(3908-3912)
IEEE DOI 1809
Generative adversarial networks, Visualization, Indexes, Standards, Image quality, Training, Natural Scene Statistics BibRef

Liu, Y., Wang, Q., Gu, Y., Kamijo, S.,
A Latent Space Understandable Generative Adversarial Network: SelfExGAN,
DICTA17(1-8)
IEEE DOI 1804
game theory, unsupervised learning, Self- ExGAN, adversarial learning, Training data BibRef

Li, X., Li, F.,
Adversarial Examples Detection in Deep Networks with Convolutional Filter Statistics,
ICCV17(5775-5783)
IEEE DOI 1802
convolution, image classification, image filtering, learning (artificial intelligence), neural nets, Training BibRef

Di, X., Yu, P.,
Max-Boost-GAN: Max Operation to Boost Generative Ability of Generative Adversarial Networks,
CEFR-LCV17(1156-1164)
IEEE DOI 1802
Convergence, Generators, Semantics, Training, Training data, Visualization BibRef

Giuffrida, M.V., Scharr, H., Tsaftaris, S.A.,
ARIGAN: Synthetic Arabidopsis Plants Using Generative Adversarial Network,
CVPPP17(2064-2071)
IEEE DOI 1802
Computational modeling, Data models, Generators, Neural networks, Training BibRef

Harada, T., Saito, K., Mukuta, Y., Ushiku, Y.,
Deep Modality Invariant Adversarial Network for Shared Representation Learning,
TASKCV17(2623-2629)
IEEE DOI 1802
Feature extraction, Games, Gaussian distribution, Generators, Training, Videos BibRef

Wang, X., Shrivastava, A., Gupta, A.,
A-Fast-RCNN: Hard Positive Generation via Adversary for Object Detection,
CVPR17(3039-3048)
IEEE DOI 1711
Detectors, Feature extraction, Object detection, Proposals, Strain, Training BibRef

Huang, X.[Xun], Li, Y.X.[Yi-Xuan], Poursaeed, O.[Omid], Hopcroft, J.[John], Belongie, S.J.[Serge J.],
Stacked Generative Adversarial Networks,
CVPR17(1866-1875)
IEEE DOI 1711
Data models, Entropy, Generators, Image reconstruction, Training BibRef

Chapter on Pattern Recognition, Clustering, Statistics, Grammars, Learning, Neural Nets, Genetic Algorithms continues in
Adversarial Networks for Image Synthesis .


Last update:Jan 29, 2023 at 20:54:24