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
Ding, X.[Xin],
Wang, Y.W.[Yong-Wei],
Xu, Z.[Zuheng],
Welch, W.J.[William J.],
Wang, Z.J.[Z. Jane],
Continuous Conditional Generative Adversarial Networks:
Novel Empirical Losses and Label Input Mechanisms,
PAMI(45), No. 7, July 2023, pp. 8143-8158.
IEEE DOI
2306
Generators, Generative adversarial networks, Benchmark testing,
Data models, Training, Task analysis, Image resolution, CcGAN,
continuous and scalar conditions
BibRef
Cao, J.[Jie],
Luo, M.[Mandi],
Yu, J.C.[Jun-Chi],
Yang, M.H.[Ming-Hsuan],
He, R.[Ran],
ScoreMix: A Scalable Augmentation Strategy for Training GANs With
Limited Data,
PAMI(45), No. 7, July 2023, pp. 8920-8935.
IEEE DOI
2306
Training, Task analysis, Training data, Image synthesis,
Generative adversarial networks, Data models, Optimization,
few-shot image-to-image translation
BibRef
Ji, F.T.[Fang-Ting],
Zhang, X.[Xin],
Zhao, J.L.[Jun-Long],
a-EGAN: a-Energy distance GAN with an early stopping rule,
CVIU(234), 2023, pp. 103748.
Elsevier DOI
2307
Generative adversarial networks, Energy distance,
Early stopping, Hypothetical testing, Evaluation metric
BibRef
Liu, S.W.[Shi-Wei],
Tian, Y.S.[Yue-Song],
Chen, T.L.[Tian-Long],
Shen, L.[Li],
Don't Be So Dense: Sparse-to-Sparse GAN Training Without Sacrificing
Performance,
IJCV(131), No. 10, October 2023, pp. 2635-2648.
Springer DOI
2309
BibRef
Nakamura, K.[Kensuke],
Korman, S.[Simon],
Hong, B.W.[Byung-Woo],
Generative adversarial networks via a composite annealing of noise
and diffusion,
PR(146), 2024, pp. 110034.
Elsevier DOI
2311
Generative adversarial networks, Optimization, Scale-space,
Noise injection, Coarse-to-fine training
BibRef
Huang, Z.C.[Zhi-Chao],
Fan, Y.B.[Yan-Bo],
Liu, C.[Chen],
Zhang, W.Z.[Wei-Zhong],
Zhang, Y.[Yong],
Salzmann, M.[Mathieu],
Süsstrunk, S.[Sabine],
Wang, J.[Jue],
Fast Adversarial Training With Adaptive Step Size,
IP(32), 2023, pp. 6102-6114.
IEEE DOI Code:
WWW Link.
2311
BibRef
Zhao, J.W.[Jiang-Wei],
Zhang, L.[Liang],
Pan, L.[Lili],
Li, H.L.[Hong-Liang],
Closed-Loop Training for Projected GAN,
SPLetters(31), 2024, pp. 106-110.
IEEE DOI
2401
BibRef
Hu, J.[Jing],
Zhong, W.W.[Wei-Wei],
Zhang, M.[Meiqi],
Kang, S.[Susu],
Yan, O.[Ouyang],
EIGAN: An explicitly and implicitly feature-aligned GAN for degraded
image classification,
PRL(178), 2024, pp. 195-201.
Elsevier DOI
2402
Learn features consistent with quality images.
Image classification, Degraded image classification,
Generative adversarial network
BibRef
Yang, X.L.[Xiu-Long],
Su, Q.[Qing],
Ji, S.H.[Shi-Hao],
Towards Bridging the Performance Gaps of Joint Energy-Based Models,
CVPR23(15732-15741)
IEEE DOI
2309
WWW Link.
BibRef
Liu, H.Z.[Hao-Zhe],
Zhang, W.T.[Wen-Tian],
Li, B.[Bing],
Wu, H.Q.[Hao-Qian],
He, N.J.[Nan-Jun],
Huang, Y.W.[Ya-Wen],
Li, Y.X.[Yue-Xiang],
Ghanem, B.[Bernard],
Zheng, Y.F.[Ye-Feng],
AdaptiveMix: Improving GAN Training via Feature Space Shrinkage,
CVPR23(16219-16229)
IEEE DOI
2309
BibRef
Olson, M.L.[Matthew L.],
Liu, S.[Shusen],
Anirudh, R.[Rushil],
Thiagarajan, J.J.[Jayaraman J.],
Bremer, P.T.[Peer-Timo],
Wong, W.K.[Weng-Keen],
Cross-GAN Auditing: Unsupervised Identification of Attribute Level
Similarities and Differences Between Pretrained Generative Models,
CVPR23(7981-7990)
IEEE DOI
2309
BibRef
Asokan, S.[Siddarth],
Seelamantula, C.S.[Chandra Sekhar],
Spider GAN: Leveraging Friendly Neighbors to Accelerate GAN Training,
CVPR23(3883-3893)
IEEE DOI
2309
BibRef
Kundu, S.[Souvik],
Sundaresan, S.[Sairam],
Pedram, M.[Massoud],
Beerel, P.A.[Peter A.],
FLOAT: Fast Learnable Once-for-All Adversarial Training for Tunable
Trade-off between Accuracy and Robustness,
WACV23(2348-2357)
IEEE DOI
2302
Training, Analytical models, Costs, Tensors, Computational modeling,
Transforms, Robustness, Embedded sensing/real-time techniques
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
Balasubramanian, S.[Sriram],
Feizi, S.[Soheil],
Towards Improved Input Masking for Convolutional Neural Networks,
ICCV23(1855-1865)
IEEE DOI
2401
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.C.[Sung-Chan],
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.Y.[Tian-Yu],
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.Y.[Ming-Yuan],
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
Zhang, J.P.[Jian-Ping],
Huang, J.T.[Jen-Tse],
Wang, W.X.[Wen-Xuan],
Li, Y.C.[Yi-Chen],
Wu, W.B.[Wei-Bin],
Wang, X.S.[Xiao-Sen],
Su, Y.X.[Yu-Xin],
Lyu, M.R.[Michael R.],
Improving the Transferability of Adversarial Samples by
Path-Augmented Method,
CVPR23(8173-8182)
IEEE DOI
2309
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
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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, Image Generation .