14.5.9.10 Adversarial Networks, Adversarial Inputs, Generative Adversarial

He, Z.[Zhi], Liu, H.[Han], Wang, Y.W.[Yi-Wen], Hu, J.[Jie],
Generative Adversarial Networks-Based Semi-Supervised Learning for Hyperspectral Image Classification,
RS(9), No. 10, 2017, pp. xx-yy.
DOI Link 1711
BibRef

He, Z.[Zhi], Wang, Y.W.[Yi-Wen], Hu, J.[Jie],
Joint Sparse and Low-Rank Multitask Learning with Laplacian-Like Regularization for Hyperspectral Classification,
RS(10), No. 2, 2018, pp. xx-yy.
DOI Link 1804
BibRef

Creswell, A., White, T., Dumoulin, V., Arulkumaran, K., Sengupta, B., Bharath, A.A.,
Generative Adversarial Networks: An Overview,
SPMag(35), No. 1, January 2018, pp. 53-65.
IEEE DOI 1801
Convolutional codes, Data models, Generators, Image resolution, Machine learning, Semantics, Signal resolution, Training data BibRef

Gao, F.[Fei], Yang, Y.[Yue], Wang, J.[Jun], Sun, J.P.[Jin-Ping], Yang, E.[Erfu], Zhou, H.Y.[Hui-Yu],
A Deep Convolutional Generative Adversarial Networks (DCGANs)-Based Semi-Supervised Method for Object Recognition in Synthetic Aperture Radar (SAR) Images,
RS(10), No. 6, 2018, pp. xx-yy.
DOI Link 1806
BibRef

Chang, W.K.[Wen-Kai], Yang, G.D.[Guo-Dong], Yu, J.Z.[Jun-Zhi], Liang, Z.Z.[Zi-Ze],
Real-time segmentation of various insulators using generative adversarial networks,
IET-CV(12), No. 5, August 2018, pp. 596-602.
DOI Link 1807
BibRef

Zhu, L., Chen, Y., Ghamisi, P., Benediktsson, J.A.,
Generative Adversarial Networks for Hyperspectral Image Classification,
GeoRS(56), No. 9, September 2018, pp. 5046-5063.
IEEE DOI 1809
Training, Hyperspectral imaging, Feature extraction, Generators, hyperspectral image (HSI) classification BibRef

Hang, R.L.[Ren-Long], Zhou, F.[Feng], Liu, Q.S.[Qing-Shan], Ghamisi, P.[Pedram],
Classification of Hyperspectral Images via Multitask Generative Adversarial Networks,
GeoRS(59), No. 2, February 2021, pp. 1424-1436.
IEEE DOI 2101
Task analysis, Generative adversarial networks, Generators, Training, Deep learning, Image reconstruction, multitask learning BibRef

Zheng, R.B.[Ruo-Bing], Wu, G.Q.[Guo-Qiang], Yan, C.[Chao], Zhang, R.[Renyu], Luo, Z.[Ze], Yan, B.P.[Bao-Ping],
Exploration in Mapping Kernel-Based Home Range Models from Remote Sensing Imagery with Conditional Adversarial Networks,
RS(10), No. 11, 2018, pp. xx-yy.
DOI Link 1812
BibRef

Borji, A.[Ali],
Pros and cons of GAN evaluation measures,
CVIU(179), 2019, pp. 41-65.
Elsevier DOI 1903
Generative adversarial nets, Generative models, Evaluation, Deep learning, Neural networks BibRef

Ding, S.[Sihao], Wallin, A.[Andreas],
Towards recovery of conditional vectors from conditional generative adversarial networks,
PRL(122), 2019, pp. 66-72.
Elsevier DOI 1904
Generative adversarial networks, Conditional, Recover BibRef

Chen, L.Q.[Liu-Qing], Wang, P.[Pan], Dong, H.[Hao], Shi, F.[Feng], Han, J.[Ji], Guo, Y.[Yike], Childs, P.R.N.[Peter R.N.], Xiao, J.[Jun], Wu, C.[Chao],
An artificial intelligence based data-driven approach for design ideation,
JVCIR(61), 2019, pp. 10-22.
Elsevier DOI 1906
Idea generation, Artificial intelligence in design, Data-driven design, Generative adversarial networks, Computational creativity BibRef

Deng, C., Yang, E., Liu, T., Li, J., Liu, W., Tao, D.,
Unsupervised Semantic-Preserving Adversarial Hashing for Image Search,
IP(28), No. 8, August 2019, pp. 4032-4044.
IEEE DOI 1907
binary codes, file organisation, image coding, image retrieval, matrix algebra, neural nets, unsupervised learning, deep learning BibRef

Wang, X., Tan, K., Du, Q., Chen, Y., Du, P.,
Caps-TripleGAN: GAN-Assisted CapsNet for Hyperspectral Image Classification,
GeoRS(57), No. 9, September 2019, pp. 7232-7245.
IEEE DOI 1909
Hyperspectral imaging, Feature extraction, Generative adversarial networks, Hidden Markov models, CapsNet, triple generative adversarial network (TripleGAN) BibRef

Mao, X.D.[Xu-Dong], Li, Q.[Qing], Xie, H.R.[Hao-Ran], Lau, R.Y.K.[Raymond Y.K.], Wang, Z.[Zhen], Smolley, S.P.[Stephen Paul],
On the Effectiveness of Least Squares Generative Adversarial Networks,
PAMI(41), No. 12, December 2019, pp. 2947-2960.
IEEE DOI 1911
BibRef
Earlier:
Least Squares Generative Adversarial Networks,
ICCV17(2813-2821)
IEEE DOI 1802
Generators, Linear programming, Task analysis, Generative adversarial networks, Stability analysis, image generation. image classification, least squares approximations, unsupervised learning, LSGANs, Stability analysis BibRef

Sun, Y.[Yubao], Chen, J.[Jiwei], Liu, Q.S.[Qing-Shan], Liu, G.C.[Guang-Can],
Learning image compressed sensing with sub-pixel convolutional generative adversarial network,
PR(98), 2020, pp. 107051.
Elsevier DOI 1911
Compressed sensing, Sub-pixel convolutional GAN, Compound loss BibRef

Wojna, Z.[Zbigniew], Ferrari, V.[Vittorio], Guadarrama, S.[Sergio], Silberman, N.[Nathan], Chen, L.C.[Liang-Chieh], Fathi, A.[Alireza], Uijlings, J.[Jasper],
The Devil is in the Decoder: Classification, Regression and GANs,
IJCV(127), No. 11-12, December 2019, pp. 1694-1706.
Springer DOI 1911
BibRef

Wei, G.[Gang], Luo, M.[Minnan], Liu, H.[Huan], Zhang, D.H.[Dong-Hui], Zheng, Q.H.[Qing-Hua],
Progressive generative adversarial networks with reliable sample identification,
PRL(130), 2020, pp. 91-98.
Elsevier DOI 2002
Generative adversarial networks, Sample selection, Unsupervised learning BibRef

Milbich, T.[Timo], Ghori, O.[Omair], Diego, F.[Ferran], Ommer, B.[Björn],
Unsupervised representation learning by discovering reliable image relations,
PR(102), 2020, pp. 107107.
Elsevier DOI 2003
Unsupervised learning, Visual representation learning, Unsupervised image classification, Mining reliable relations, Divide-and-conquer BibRef

Lorenz, D.[Dominik], Bereska, L.[Leonard], Milbich, T.[Timo], Ommer, B.[Bjorn],
Unsupervised Part-Based Disentangling of Object Shape and Appearance,
CVPR19(10947-10956).
IEEE DOI 2002
BibRef

Peng, Y.[Ye], Zhao, W.T.[Wen-Tao], Cai, W.[Wei], Su, J.S.[Jin-Shu], Han, B.[Biao], Liu, Q.A.[Qi-Ang],
Evaluating Deep Learning for Image Classification in Adversarial Environment,
IEICE(E103-D), No. 4, April 2020, pp. 825-837.
WWW Link. 2004
BibRef

Feng, J.[Jie], Feng, X.L.[Xue-Liang], Chen, J.T.[Jian-Tong], Cao, X.H.[Xiang-Hai], Zhang, X.R.[Xiang-Rong], Jiao, L.C.[Li-Cheng], Yu, T.[Tao],
Generative Adversarial Networks Based on Collaborative Learning and Attention Mechanism for Hyperspectral Image Classification,
RS(12), No. 7, 2020, pp. xx-yy.
DOI Link 2004
BibRef

Qi, M., Wang, Y., Li, A., Luo, J.,
STC-GAN: Spatio-Temporally Coupled Generative Adversarial Networks for Predictive Scene Parsing,
IP(29), 2020, pp. 5420-5430.
IEEE DOI 2004
Predictive Scene Parsing, Generative Adversarial Networks, Coupled Architecture, Spatio-Temporal Features BibRef

Newson, A.[Alasdair], Almansa, A.[Andrés], Gousseau, Y.[Yann], Ladjal, S.[Saïd],
Processing Simple Geometric Attributes with Autoencoders,
JMIV(62), No. 3, April 2020, pp. 293-312.
Springer DOI 2004
BibRef

Zhang, L.[Long], Zhao, J.Y.[Jie-Yu], Ye, X.L.[Xu-Lun], Chen, Y.[Yu],
Cooperation: A new force for boosting generative adversarial nets with dual-network structure,
IET-IPR(14), No. 6, 11 May 2020, pp. 1073-1080.
DOI Link 2005
BibRef

Qi, G.J.[Guo-Jun],
Loss-Sensitive Generative Adversarial Networks on Lipschitz Densities,
IJCV(128), No. 5, May 2020, pp. 1118-1140.
Springer DOI 2005
BibRef

Ravat, R.S.[Rajvardhan Singh], Verma, Y.[Yashaswi],
A retrieval-based approach for diverse and image-specific adversary selection,
MultInfoRetr(9), No. 2, June 2020, pp. 125-133.
Springer DOI 2005
BibRef

Wan, D.[Diwen], Shen, F.M.[Fu-Min], Liu, L.[Li], Zhu, F.[Fan], Huang, L.[Lei], Yu, M.Y.[Meng-Yang], Shen, H.T.[Heng Tao], Shao, L.[Ling],
Deep quantization generative networks,
PR(105), 2020, pp. 107338.
Elsevier DOI 2006
Compression, Acceleration, Generative models, Network quantization BibRef

Zhang, Z.H.[Zhi-Hong], Zeng, Y.B.[Yang-Bin], Bai, L.[Lu], Hu, Y.Q.[Yi-Qun], Wu, M.H.[Mei-Hong], Wang, S.[Shuai], Hancock, E.R.[Edwin R.],
Spectral bounding: Strictly satisfying the 1-Lipschitz property for generative adversarial networks,
PR(105), 2020, pp. 107179.
Elsevier DOI 2006
Generative adversarial networks, 1-Lipschitz constraint, Spectral bounding, Image generation BibRef

Mishra, D.[Deepak], Jayendran, A.[Aravind], Prathosh, A.P.,
Effect of the Latent Structure on Clustering With GANs,
SPLetters(27), 2020, pp. 900-904.
IEEE DOI 2006
Random variables, Generative adversarial networks, Generators, Data models, neural networks BibRef

Song, J.K.[Jing-Kuan], He, T.[Tao], Gao, L.L.[Lian-Li], Xu, X.[Xing], Hanjalic, A.[Alan], Shen, H.T.[Heng Tao],
Unified Binary Generative Adversarial Network for Image Retrieval and Compression,
IJCV(128), No. 8-9, September 2020, pp. 2243-2264.
Springer DOI 2008
BibRef

Abbasnejad, M.E.[M. Ehsan], Shi, J.[Javen], van den Hengel, A.J.[Anton J.], Liu, L.Q.[Ling-Qiao],
GADE: A Generative Adversarial Approach to Density Estimation and its Applications,
IJCV(128), No. 10-11, November 2020, pp. 2731-2743.
Springer DOI 2009
BibRef

Abbasnejad, M.E.[M. Ehsan], Shi, J.Q.F.[Javen Qin-Feng], van den Hengel, A.J.[Anton J.], Liu, L.Q.[Ling-Qiao],
A Generative Adversarial Density Estimator,
CVPR19(10774-10783).
IEEE DOI 2002
BibRef

Li, W.[Wei], Fan, L.[Li], Wang, Z.Y.[Zhen-Yu], Ma, C.[Chao], Cui, X.H.[Xiao-Hui],
Tackling mode collapse in multi-generator GANs with orthogonal vectors,
PR(110), 2021, pp. 107646.
Elsevier DOI 2011
GANs, Mode collapse, Multiple generators, Orthogonal vectors, Minimax formula BibRef

Liu, K.L.[Kang-Lin], Qiu, G.P.[Guo-Ping], Tang, W.M.[Wen-Ming], Zhou, F.[Fei],
Spectral Regularization for Combating Mode Collapse in GANs,
IVC(104), 2020, pp. 104005.
Elsevier DOI 2012
BibRef
Earlier: ICCV19(6381-6389)
IEEE DOI 2004
Spectral regularization, Generative adversarial networks (GANs), Mode collapse. neural nets, singular value decomposition, SR-GANs, SN-GANs, spectral normalized GANs, Optimization BibRef

Wang, J.[Jue], Cherian, A.[Anoop],
Discriminative Video Representation Learning Using Support Vector Classifiers,
PAMI(43), No. 2, February 2021, pp. 420-433.
IEEE DOI 2101
BibRef
And:
Learning Discriminative Video Representations Using Adversarial Perturbations,
ECCV18(II: 716-733).
Springer DOI 1810
Support vector machines, Feature extraction, Trajectory, Task analysis, Image recognition, deep learning BibRef

Wang, J.[Jue], Cherian, A.[Anoop], Porikli, F.M.[Fatih M.], Gould, S.,
Video Representation Learning Using Discriminative Pooling,
CVPR18(1149-1158)
IEEE DOI 1812
Support vector machines, Computational modeling, Task analysis, Feature extraction, Data models, Kernel BibRef

Li, X.[Xiao], Fang, M.[Min], Li, H.[Haikun],
Bias alleviating generative adversarial network for generalized zero-shot classification,
IVC(105), 2021, pp. 104077.
Elsevier DOI 2101
Generalized zero shot classification, Generative adversarial network, Unseen visual prototypes, Semantic relationships BibRef

Zhang, M.[Man], Zhou, Y.[Yong], Zhao, J.Q.[Jia-Qi], Xia, S.X.[Shi-Xiong], Wang, J.Q.[Jia-Qi], Huang, Z.Z.[Zi-Zheng],
Semi-supervised blockwisely architecture search for efficient lightweight generative adversarial network,
PR(112), 2021, pp. 107794.
Elsevier DOI 2102
Semi-supervised, GANs, Network architecture search, Image generation, Image classification BibRef

Hu, P.[Peng], Peng, X.[Xi], Zhu, H.Y.[Hong-Yuan], Lin, J.[Jie], Zhen, L.L.[Liang-Li], Wang, W.[Wei], Peng, D.Z.[De-Zhong],
Cross-modal discriminant adversarial network,
PR(112), 2021, pp. 107734.
Elsevier DOI 2102
Adversarial learning, Cross-modal representation learning, Cross-modal retrieval, Discriminant adversarial network, Latent common space BibRef

Gnanha, A.T.[Aurele Tohokantche], Cao, W.M.[Wen-Ming], Mao, X.D.[Xu-Dong], Wu, S.[Si], Wong, H.S.[Hau-San], Li, Q.[Qing],
The residual generator: An improved divergence minimization framework for GAN,
PR(121), 2022, pp. 108222.
Elsevier DOI 2109
Generative adversarial networks, Image synthesis, Deep learning BibRef

Tan, D.N.[Da-Ning], Liu, Y.[Yu], Li, G.[Gang], Yao, L.[Libo], Sun, S.[Shun], He, Y.[You],
Serial GANs: A Feature-Preserving Heterogeneous Remote Sensing Image Transformation Model,
RS(13), No. 19, 2021, pp. xx-yy.
DOI Link 2110
BibRef

Marín, J.[Javier], Escalera, S.[Sergio],
SSSGAN: Satellite Style and Structure Generative Adversarial Networks,
RS(13), No. 19, 2021, pp. xx-yy.
DOI Link 2110
A generative model of high resolution satellite imagery to support image segmentation. BibRef

Liu, Y.[Ying], Fan, H.[Heng], Yuan, X.H.[Xiao-Hui], Xiang, J.H.[Jin-Hai],
GL-GAN: Adaptive global and local bilevel optimization for generative adversarial network,
PR(123), 2022, pp. 108375.
Elsevier DOI 2112
Generative adversarial networks (GAN), Global and local bilevel optimization, Ada-OP, Image generation BibRef

Baykal, G.[Gulcin], Ozcelik, F.[Furkan], Unal, G.[Gozde],
Exploring DeshuffleGANs in Self-Supervised Generative Adversarial Networks,
PR(122), 2022, pp. 108244.
Elsevier DOI 2112
Self-Supervised generative adversarial networks, Generative adversarial networks, Self-supervised learning, Deshuffling BibRef

Yu, S.[Simin], Zhang, K.[Kuntian], Xiao, C.[Chuan], Huang, J.Z.[Joshua Zhexue], Li, M.J.J.[Mark Jun-Jie], Onizuka, M.[Makoto],
HSGAN: Reducing mode collapse in GANs by the latent code distance of homogeneous samples,
CVIU(214), 2022, pp. 103314.
Elsevier DOI 2112
Generative adversarial networks, Mode collapse, Image generation BibRef

Zhao, T.T.[Ting-Ting], Wang, Y.[Ying], Li, G.X.[Gui-Xi], Kong, L.[Le], Chen, Y.[Yarui], Wang, Y.[Yuan], Xie, N.[Ning], Yang, J.[Jucheng],
A model-based reinforcement learning method based on conditional generative adversarial networks,
PRL(152), 2021, pp. 18-25.
Elsevier DOI 2112
BibRef

Chinbat, V.[Vanchinbal], Bae, S.H.[Seung-Hwan],
GA3N: Generative adversarial AutoAugment network,
PR(127), 2022, pp. 108637.
Elsevier DOI 2205
Data augmentation, AutoAugment, Generative adversarial network, Classification, Deep learning, Adversarial learning BibRef

Mu, J.Z.[Jin-Zhen], Chen, C.Y.[Chun-Yan], Zhu, W.S.[Wen-Shan], Li, S.[Shuang], Zhou, Y.[Yan],
Taming mode collapse in generative adversarial networks using cooperative realness discriminators,
IET-IPR(16), No. 8, 2022, pp. 2240-2262.
DOI Link 2205
BibRef

Struski, L.[Lukasz], Knop, S.[Szymon], Spurek, P.[Przemyslaw], Daniec, W.[Wiktor], Tabor, J.[Jacek],
LocoGAN: Locally convolutional GAN,
CVIU(221), 2022, pp. 103462.
Elsevier DOI 2206
GAN, Generative models, Fully convolutional architecture, Textures BibRef

Bai, J.[Jing], Lu, J.W.[Jia-Wei], Xiao, Z.[Zhu], Chen, Z.[Zheng], Jiao, L.C.[Li-Cheng],
Generative Adversarial Networks Based on Transformer Encoder and Convolution Block for Hyperspectral Image Classification,
RS(14), No. 14, 2022, pp. xx-yy.
DOI Link 2208
BibRef

Li, C.X.[Chong-Xuan], Xu, K.[Kun], Zhu, J.[Jun], Liu, J.S.[Jia-Shuo], Zhang, B.[Bo],
Triple Generative Adversarial Networks,
PAMI(44), No. 12, December 2022, pp. 9629-9640.
IEEE DOI 2212
Generators, Generative adversarial networks, Task analysis, Semisupervised learning, Games, Entropy, Linear programming, conditional image generation BibRef

Xu, T.[Teng], Yan, H.Y.[Hong-Yong], Yu, H.[Hui], Zhang, Z.Y.[Zhi-Yong],
Removing Time Dispersion from Elastic Wave Modeling with the pix2pix Algorithm Based on cGAN,
RS(15), No. 12, 2023, pp. xx-yy.
DOI Link 2307
BibRef

Buzuti, L.F.[Lucas F.], Thomaz, C.E.[Carlos E.],
Fréchet AutoEncoder Distance: A new approach for evaluation of Generative Adversarial Networks,
CVIU(235), 2023, pp. 103768.
Elsevier DOI 2310
Generative adversarial networks, Autoencoder, Feature extraction, Fréchet distance, Evaluation, Measure BibRef

Chen, Y.M.[Yan-Ming], Xu, J.H.[Jia-Hao], An, Z.[Zhulin], Zhuang, F.Z.[Fu-Zhen],
Multi-scale conditional reconstruction generative adversarial network,
IVC(141), 2024, pp. 104885.
Elsevier DOI 2402
Generative adversarial network, Unsupervised generation, Multi-scale instance, Reconstructed losses BibRef

Li, J.Z.[Jin-Zhong], Zeng, H.[Huan], Xiao, C.W.[Cun-Wei], Ouyang, C.J.[Chun-Juan], Liu, H.[Hua],
Listwise learning to rank method combining approximate NDCG ranking indicator with Conditional Generative Adversarial Networks,
PRL(179), 2024, pp. 31-37.
Elsevier DOI 2403
Learning to rank, Conditional Generative Adversarial Networks, Approximate Normalized Discounted Cumulative Gain, Ranking indicator BibRef

Cai, J.Y.[Jin-Yu], Zhang, Y.H.[Yun-He], Wang, S.P.[Shi-Ping], Fan, J.C.[Ji-Cong], Guo, W.Z.[Wen-Zhong],
Wasserstein Embedding Learning for Deep Clustering: A Generative Approach,
MultMed(26), 2024, pp. 7567-7580.
IEEE DOI 2405
Integrate robust generative models with clustering. Training, Data models, Generative adversarial networks, Clustering methods, Task analysis, Deep learning, Decoding, auto-encoder BibRef

Luo, Y.X.[Yi-Xin], Yang, Z.W.[Zhou-Wang],
DynGAN: Solving Mode Collapse in GANs With Dynamic Clustering,
PAMI(46), No. 8, August 2024, pp. 5493-5503.
IEEE DOI 2407
Generators, Training, Data models, Computational modeling, Unsupervised learning, Manifolds BibRef

Choi, J.[Jaewoong], Hwang, G.[Geonho], Cho, H.[Hyunsoo], Kang, M.J.[Myung-Joo],
Analyzing the latent space of GAN through local dimension estimation for disentanglement evaluation,
PR(157), 2025, pp. 110914.
Elsevier DOI 2409
Generative Adversarial Network, Disentanglement, Semantic factorization, Dimension estimation, Grassmannian BibRef

Zhang, B.C.[Bei-Chen], Li, L.[Liang], Wang, S.H.[Shu-Hui], Cai, S.F.[Shao-Fei], Zha, Z.J.[Zheng-Jun], Tian, Q.[Qi], Huang, Q.M.[Qing-Ming],
Inductive State-Relabeling Adversarial Active Learning With Heuristic Clique Rescaling,
PAMI(46), No. 12, December 2024, pp. 9780-9796.
IEEE DOI 2411
Uncertainty, Annotations, Task analysis, Data models, Training, Redundancy, Generators, Active learning, adversarial learning, data diversity BibRef

Zhang, B.C.[Bei-Chen], Li, L.[Liang], Yang, S., Wang, S.H.[Shu-Hui], Zha, Z.J.[Zheng-Jun], Huang, Q.M.[Qing-Ming],
State-Relabeling Adversarial Active Learning,
CVPR20(8753-8762)
IEEE DOI 2008
Task analysis, Uncertainty, Generators, Data models, Computational modeling, Image reconstruction, Learning systems BibRef

Fan, L.[Lei], Wu, Y.[Ying],
Avoiding Lingering in Learning Active Recognition by Adversarial Disturbance,
WACV23(4601-4610)
IEEE DOI 2302
Training, Working environment noise, Perturbation methods, Games, Turning, Cameras, Robotics BibRef

Sapkota, S.[Suman], Khanal, B.[Bidur], Bhattarai, B.[Binod], Khanal, B.[Bishesh], Kim, T.K.[Tae-Kyun],
Label Geometry Aware Discriminator for Conditional Generative Adversarial Networks,
ICPR22(2914-2920)
IEEE DOI 2212
Measurement, Manifolds, Geometry, Additives, Generative adversarial networks, Generators, Active appearance model BibRef

Ding, F.[Fei], Yang, Y.[Yin], Luo, F.[Feng],
Clustering by Directly Disentangling Latent Space,
ICIP22(341-345)
IEEE DOI 2211
Clustering methods, Impedance matching, Generative adversarial networks, Generators, Task analysis, latent space BibRef

Akimoto, N., Kasai, S., Hayashi, M., Aoki, Y.,
360-Degree Image Completion by Two-Stage Conditional GANs,
ICIP19(4704-4708)
IEEE DOI 1910
Generative adversarial networks, 360 degrees, image completion, extrapolation BibRef

Yazici, Y.[Yasin], Lecouat, B.[Bruno], Yap, K.H.[Kim Hui], Winkler, S.[Stefan], Piliouras, G.[Georgios], Chandrasekhar, V.[Vijay], Foo, C.S.[Chuan-Sheng],
Mixed Membership Generative Adversarial Networks,
ICIP22(1026-1030)
IEEE DOI 2211
Analytical models, Image resolution, Generative adversarial networks, Generators, Data models, mixture membership models BibRef

Huang, G.L.[Gi-Luen], Wu, P.Y.[Pei-Yuan],
CTGAN: Cloud Transformer Generative Adversarial Network,
ICIP22(511-515)
IEEE DOI 2211
Satellites, Codes, Clouds, Feature extraction, Transformers, Generative adversarial networks, Remote sensing, FormoSat-2 satellite BibRef

Lee, G.[Gayoung], Kim, H.[Hyunsu], Kim, J.[Junho], Kim, S.[Seonghyeon], Ha, J.W.[Jung-Woo], Choi, Y.[Yunjey],
Generator Knows What Discriminator Should Learn in Unconditional GANs,
ECCV22(XVII:406-422).
Springer DOI 2211
BibRef

Balakrishnan, G.[Guha], Gadde, R.[Raghudeep], Martinez, A.[Aleix], Perona, P.[Pietro],
Rayleigh EigenDirections (REDs): Nonlinear GAN Latent Space Traversals for Multidimensional Features,
ECCV22(XVII:510-526).
Springer DOI 2211
BibRef

Lee, J.[Junghyuk], Lee, J.S.[Jong-Seok],
TREND: Truncated Generalized Normal Density Estimation of Inception Embeddings for GAN Evaluation,
ECCV22(XXIII:87-103).
Springer DOI 2211
BibRef

Chen, Z.[Zikun], Jiang, R.[Ruowei], Duke, B.[Brendan], Zhao, H.[Han], Aarabi, P.[Parham],
Exploring Gradient-Based Multi-directional Controls in GANs,
ECCV22(XXIII:104-119).
Springer DOI 2211
BibRef

Avrahami, O.[Omri], Lischinski, D.[Dani], Fried, O.[Ohad],
GAN Cocktail: Mixing GANs Without Dataset Access,
ECCV22(XXIII:205-221).
Springer DOI 2211
BibRef

Lin, C.Y.[Chien-Yu], Prabhu, A.[Anish], Merth, T.[Thomas], Mehta, S.[Sachin], Ranjan, A.[Anurag], Horton, M.[Maxwell], Rastegari, M.[Mohammad],
SPIN: An Empirical Evaluation on Sharing Parameters of Isotropic Networks,
ECCV22(XI:553-568).
Springer DOI 2211
BibRef

Chan, E.R.[Eric R.], Lin, C.Z.[Connor Z.], Chan, M.A.[Matthew A.], Nagano, K.[Koki], Pan, B.[Boxiao], de Mello, S.[Shalini], Gallo, O.[Orazio], Guibas, L.J.[Leonidas J.], Tremblay, J.[Jonathan], Khamis, S.[Sameh], Karras, T.[Tero], Wetzstein, G.[Gordon],
Efficient Geometry-aware 3D Generative Adversarial Networks,
CVPR22(16102-16112)
IEEE DOI 2210
Solid modeling, Shape, Image synthesis, Network architecture, Rendering (computer graphics), Rapid prototyping, Vision + graphics BibRef

Ni, Y.[Yao], Koniusz, P.[Piotr], Hartley, R.I.[Richard I.], Nock, R.[Richard],
Manifold Learning Benefits GANs,
CVPR22(11255-11264)
IEEE DOI 2210
Manifolds, Image coding, Statistical analysis, Refining, Noise reduction, Machine learning, Statistical methods BibRef

Fontanini, T.[Tomaso], Praticò, C.[Claudio], Prati, A.[Andrea],
Towards Latent Space Optimization of GANs Using Meta-Learning,
CIAP22(I:646-657).
Springer DOI 2205
BibRef

Tzelepis, C.[Christos], Tzimiropoulos, G.[Georgios], Patras, I.[Ioannis],
WarpedGANSpace: Finding non-linear RBF paths in GAN latent space,
ICCV21(6373-6382)
IEEE DOI 2203
Visualization, Codes, Protocols, Art, Buildings, Inspection, Neural generative models, Explainable AI BibRef

Issenhuth, T.[Thibaut], Tanielian, U.[Ugo], Picard, D.[David], Mary, J.[Jérémie],
Latent reweighting, an almost free improvement for GANs,
WACV22(3574-3583)
IEEE DOI 2202
Visualization, Computational modeling, Architecture, Neural networks, Fitting, Sampling methods, GANs BibRef

Collier, E.[Edward], Mukhopadhyay, S.[Supratik],
SimilarityGAN: Using Similarity to Loosen Structural Constraints in Generative Adversarial Models,
DICTA21(1-8)
IEEE DOI 2201
Digital images, Computational modeling, Generative adversarial networks, Generators, Structural Constraint BibRef

Nissani Nissensohn, D.N.[Daniel N.],
A Simple Generative Network,
ISVC21(II:242-250).
Springer DOI 2112
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Liu, H.F.[Hua-Feng], Wang, J.Q.[Jia-Qi], Jing, L.P.[Li-Ping],
Cluster-wise Hierarchical Generative Model for Deep Amortized Clustering,
CVPR21(15104-15113)
IEEE DOI 2111
Measurement, Adaptation models, Computational modeling, Trajectory BibRef

Hyun, S.[Sangeek], Kim, J.[Jihwan], Heo, J.P.[Jae-Pil],
Self-Supervised Video GANs: Learning for Appearance Consistency and Motion Coherency,
CVPR21(10821-10830)
IEEE DOI 2111
Force, Benchmark testing, Generative adversarial networks, Generators BibRef

Kim, K.[Kwanyoung], Park, D.[Dongwon], Kim, K.I.[Kwang In], Chun, S.Y.[Se Young],
Task-Aware Variational Adversarial Active Learning,
CVPR21(8162-8171)
IEEE DOI 2111
Deep learning, Limiting, Costs, Semantics, Benchmark testing, Generative adversarial networks, Data models BibRef

Yang, H.T.[Hui-Ting], Chai, L.Y.[Liang-Yu], Wen, Q.[Qiang], Zhao, S.[Shuang], Sun, Z.X.[Zi-Xun], He, S.F.[Sheng-Feng],
Discovering Interpretable Latent Space Directions of GANs Beyond Binary Attributes,
CVPR21(12172-12180)
IEEE DOI 2111
Correlation, Codes, Semantics, Generative adversarial networks, Task analysis BibRef

Hu, Q.J.[Qian-Jiang], Wang, X.[Xiao], Hu, W.[Wei], Qi, G.J.[Guo-Jun],
AdCo: Adversarial Contrast for Efficient Learning of Unsupervised Representations from Self-Trained Negative Adversaries,
CVPR21(1074-1083)
IEEE DOI 2111
Learning systems, Codes, Graphics processing units, Task analysis BibRef

Daunhawer, I.[Imant], Sutter, T.M.[Thomas M.], Marcinkevics, R.[Ricards], Vogt, J.E.[Julia E.],
Self-supervised Disentanglement of Modality-Specific and Shared Factors Improves Multimodal Generative Models,
GCPR20(459-473).
Springer DOI 2110
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Li, Z.Q.[Zi-Qiang], Tao, R.[Rentuo], Niu, H.J.[Hong-Jing], Yue, M.D.[Ming-Dao], Li, B.[Bin],
Interpreting the Latent Space of GANs via Correlation Analysis for Controllable Concept Manipulation,
ICPR21(1942-1948)
IEEE DOI 2105
How does the GAN really work? Drugs, Visualization, Analytical models, Correlation, Statistical analysis, Image synthesis, Semantics BibRef

Zheng, W.B.[Wen-Bo], Yan, L.[Lan], Wang, F.Y.[Fei-Yue], Gou, C.[Chao],
Learning from the Negativity: Deep Negative Correlation Meta-learning for Adversarial Image Classification,
MMMod21(I:531-540).
Springer DOI 2106
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Katsumata, K.[Kai], Kobayashi, R.[Ryoga],
Uncertainty Estimates in Deep Generative Models Using Gaussian Processes,
ISVC20(I:121-132).
Springer DOI 2103
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Saberi, I.[Iman], Faghih, F.[Fathiyeh],
Self-competitive Neural Networks,
ISVC20(I:15-26).
Springer DOI 2103
BibRef

Ayadi, I.[Imen], Turinici, G.[Gabriel],
Stochastic Runge-Kutta methods and adaptive SGD-G2 stochastic gradient descent,
ICPR21(8220-8227)
IEEE DOI 2105
Adaptive systems, Image databases, Neural networks, Minimization, Standards, Optimization BibRef

Turinici, G.[Gabriel],
Convergence Dynamics of Generative Adversarial Networks: The Dual Metric Flows,
CADL20(619-634).
Springer DOI 2103
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Roziere, B.[Baptiste], Teytaud, F.[Fabien], Hosu, V.[Vlad], Lin, H.[Hanhe], Rapin, J.[Jeremy], Zameshina, M.[Mariia], Teytaud, O.[Olivier],
EvolGAN: Evolutionary Generative Adversarial Networks,
ACCV20(IV:679-694).
Springer DOI 2103
BibRef

Wang, F.[Fan], Liu, H.D.[Hui-Dong], Samaras, D.[Dimitris], Chen, C.[Chao],
Topogan: A Topology-aware Generative Adversarial Network,
ECCV20(III:118-136).
Springer DOI 2012
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Xu, K.D.[Kai-Di], Zhang, G.Y.[Gao-Yuan], Liu, S.J.[Si-Jia], Fan, Q.F.[Quan-Fu], Sun, M.S.[Meng-Shu], Chen, H.G.[Hong-Ge], Chen, P.Y.[Pin-Yu], Wang, Y.Z.[Yan-Zhi], Lin, X.[Xue],
Adversarial T-shirt! Evading Person Detectors in a Physical World,
ECCV20(V:665-681).
Springer DOI 2011
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Qu, H.[Hui], Zhang, Y.K.[Yi-Kai], Chang, Q.[Qi], Yan, Z.N.[Zhen-Nan], Chen, C.[Chao], Metaxas, D.N.[Dimitris N.],
Learn Distributed GAN with Temporary Discriminators,
ECCV20(XXVII:175-192).
Springer DOI 2011
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Peng, X., Bouzerdoum, A.[Abdesselam], Phung, S.L.[Son L.],
Infer the Input to the Generator of Auxiliary Classifier Generative Adversarial Networks,
ICIP20(76-80)
IEEE DOI 2011
Generators, Convolutional codes, Data models, Optimized production technology, Linear programming, ACGANs, encoder BibRef

Zhu, X.Q.[Xin-Qi], Xu, C.[Chang], Tao, D.C.[Da-Cheng],
Learning Disentangled Representations with Latent Variation Predictability,
ECCV20(X:684-700).
Springer DOI 2011
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Peebles, W.[William], Peebles, J.[John], Zhu, J.Y.[Jun-Yan], Efros, A.[Alexei], Torralba, A.B.[Antonio B.],
The Hessian Penalty: A Weak Prior for Unsupervised Disentanglement,
ECCV20(VI:581-597).
Springer DOI 2011
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Zhang, X.B.[Xiao-Bing], Lu, S.J.[Shi-Jian], Gong, H.G.[Hai-Gang], Luo, Z.P.[Zhi-Peng], Liu, M.[Ming],
Amln: Adversarial-based Mutual Learning Network for Online Knowledge Distillation,
ECCV20(XII: 158-173).
Springer DOI 2010
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Srinivasan, P.P., Mildenhall, B., Tancik, M., Barron, J.T., Tucker, R., Snavely, N.,
Lighthouse: Predicting Lighting Volumes for Spatially-Coherent Illumination,
CVPR20(8077-8086)
IEEE DOI 2008
Lighting, Rendering (computer graphics), Geometry, Solid modeling, Cameras, Light sources BibRef

Pumarola, A.[Albert], Popov, S.[Stefan], Moreno-Noguer, F.[Francesc], Ferrari, V.[Vittorio],
C-Flow: Conditional Generative Flow Models for Images and 3D Point Clouds,
CVPR20(7946-7955)
IEEE DOI 2008
Couplings, Data models, Shape, Solid modeling, Computational modeling BibRef

Chong, M.J.[Min Jin], Forsyth, D.A.[David A.],
Effectively Unbiased FID and Inception Score and Where to Find Them,
CVPR20(6069-6078)
IEEE DOI 2008
Fréchet Inception Distance (FID) and the Inception Score (IS)/ Generators, Computational modeling, Monte Carlo methods, Extrapolation, Entropy, Standards BibRef

Gu, J., Shen, Y., Zhou, B.,
Image Processing Using Multi-Code GAN Prior,
CVPR20(3009-3018)
IEEE DOI 2008
Image reconstruction, Task analysis, Generators, Semantics, Image resolution BibRef

Mopuri, K.R., Shaj, V., Babu, R.V.,
Adversarial Fooling Beyond 'Flipping the Label',
AML-CV20(3374-3382)
IEEE DOI 2008
Measurement, Semantics, Visualization, Computational modeling, Dogs, Perturbation methods, Analytical models BibRef

Agarwal, A., Vatsa, M., Singh, R., Ratha, N.K.,
Noise is Inside Me! Generating Adversarial Perturbations with Noise Derived from Natural Filters,
AML-CV20(3354-3363)
IEEE DOI 2008
Databases, Cameras, Perturbation methods, Computational modeling, Image edge detection, Data mining, Machine learning BibRef

Wang, Y., Chen, Y., Zhang, X., Sun, J., Jia, J.,
Attentive Normalization for Conditional Image Generation,
CVPR20(5093-5102)
IEEE DOI 2008
Semantics, Layout, Image generation, Generative adversarial networks, Correlation BibRef

Zhao, Z., Liu, Z., Larson, M.,
Towards Large Yet Imperceptible Adversarial Image Perturbations With Perceptual Color Distance,
CVPR20(1036-1045)
IEEE DOI 2008
Image color analysis, Perturbation methods, Optimization, Semantics, Visualization, Extraterrestrial measurements BibRef

Ghojogh, B.[Benyamin], Karray, F.[Fakhri], Crowley, M.[Mark],
Theoretical Insights into the Use of Structural Similarity Index in Generative Models and Inferential Autoencoders,
ICIAR20(II:112-117).
Springer DOI 2007
BibRef

Sinha, S., Ebrahimi, S., Darrell, T.J.,
Variational Adversarial Active Learning,
ICCV19(5971-5980)
IEEE DOI 2004
image classification, image segmentation, learning (artificial intelligence), neural nets, Labeling BibRef

dos Santos, C.N.[Cicero Nogueira], Mroueh, Y.[Youssef], Padhi, I.[Inkit], Dognin, P.[Pierre],
Learning Implicit Generative Models by Matching Perceptual Features,
ICCV19(4460-4469)
IEEE DOI 2004
convolutional neural nets, feature extraction, image matching, learning (artificial intelligence), implicit generative models, Method of moments BibRef

Xiao, C., Deng, R., Li, B., Lee, T., Edwards, B., Yi, J., Song, D., Liu, M., Molloy, I.,
AdvIT: Adversarial Frames Identifier Based on Temporal Consistency in Videos,
ICCV19(3967-3976)
IEEE DOI 2004
feature extraction, image classification, image motion analysis, image sequences, learning (artificial intelligence), neural nets, Adaptive optics BibRef

Shu, H.[Han], Wang, Y.H.[Yun-He], Jia, X.[Xu], Han, K.[Kai], Chen, H.T.[Han-Ting], Xu, C.J.[Chun-Jing], Tian, Q.[Qi], Xu, C.[Chang],
Co-Evolutionary Compression for Unpaired Image Translation,
ICCV19(3234-3243)
IEEE DOI 2004
computational complexity, convolution, evolutionary computation, feature extraction, image coding, Convolution BibRef

Sadeghi, B., Yu, R., Boddeti, V.N.[Vishnu Naresh],
On the Global Optima of Kernelized Adversarial Representation Learning,
ICCV19(7970-7978)
IEEE DOI 2004
iterative methods, learning (artificial intelligence), minimax techniques, neural nets, iterative minimax optimization, Convergence BibRef

Xiang, Y., Fu, Y., Ji, P., Huang, H.,
Incremental Learning Using Conditional Adversarial Networks,
ICCV19(6618-6627)
IEEE DOI 2004
convolutional neural nets, feature extraction, image recognition, image representation, learning (artificial intelligence), BibRef

Mullick, S.S., Datta, S., Das, S.,
Generative Adversarial Minority Oversampling,
ICCV19(1695-1704)
IEEE DOI 2004
image classification, image sampling, learning (artificial intelligence), neural nets, Tuning BibRef

Zhao, P., Liu, S., Chen, P., Hoang, N., Xu, K., Kailkhura, B., Lin, X.,
On the Design of Black-Box Adversarial Examples by Leveraging Gradient-Free Optimization and Operator Splitting Method,
ICCV19(121-130)
IEEE DOI 2004
Bayes methods, image classification, image retrieval, learning (artificial intelligence), optimisation, Estimation BibRef

Pande, S., Banerjee, A., Kumar, S., Banerjee, B., Chaudhuri, S.,
An Adversarial Approach to Discriminative Modality Distillation for Remote Sensing Image Classification,
CroMoL19(4571-4580)
IEEE DOI 2004
feature extraction, geophysical image processing, image classification, image representation, Hyperspectral images BibRef

Liu, H., Ji, R., Li, J., Zhang, B., Gao, Y., Wu, Y., Huang, F.,
Universal Adversarial Perturbation via Prior Driven Uncertainty Approximation,
ICCV19(2941-2949)
IEEE DOI 2004
gradient methods, Monte Carlo methods, neural nets, sampling methods, stochastic processes, deep learning models, Laplace equations BibRef

Mahdizadehaghdam, S., Panahi, A., Krim, H.,
Sparse Generative Adversarial Network,
CEFRL19(3063-3071)
IEEE DOI 2004
feature extraction, learning (artificial intelligence), signal reconstruction, signal representation, vectors, deep learning BibRef

Krishnan, D., Teterwak, P., Sarna, A., Maschinot, A., Liu, C., Belanger, D., Freeman, W.,
Boundless: Generative Adversarial Networks for Image Extension,
ICCV19(10520-10529)
IEEE DOI 2004
image colour analysis, image restoration, image texture, neural nets, computational photography, computer graphics, Context modeling BibRef

Kundu, J.N., Gor, M., Agrawal, D., Radhakrishnan, V.B.,
GAN-Tree: An Incrementally Learned Hierarchical Generative Framework for Multi-Modal Data Distributions,
ICCV19(8190-8199)
IEEE DOI 2004
learning (artificial intelligence), neural nets, pattern clustering, tree data structures, Task analysis BibRef

Shocher, A.[Assaf], Gandelsman, Y.[Yossi], Mosseri, I.[Inbar], Yarom, M.[Michal], Irani, M.[Michal], Freeman, W.T.[William T.], Dekel, T.[Tali],
Semantic Pyramid for Image Generation,
CVPR20(7455-7464)
IEEE DOI 2008
Semantics, Feature extraction, Image reconstruction, Generators, Task analysis, Aerospace electronics BibRef

Shaham, T.R., Dekel, T., Michaeli, T.,
SinGAN: Learning a Generative Model From a Single Natural Image,
ICCV19(4569-4579)
IEEE DOI 2004
Award, Marr Prize. image classification, image segmentation, image texture, learning (artificial intelligence), SinGAN, Computational modeling BibRef

Raj, A., Li, Y., Bresler, Y.,
GAN-Based Projector for Faster Recovery With Convergence Guarantees in Linear Inverse Problems,
ICCV19(5601-5610)
IEEE DOI 2004
compressed sensing, computational complexity, Gaussian processes, gradient methods, image reconstruction, inverse problems, Approximation algorithms BibRef

Shama, F., Mechrez, R., Shoshan, A., Zelnik-Manor, L.,
Adversarial Feedback Loop,
ICCV19(3204-3213)
IEEE DOI 2004
feature extraction, image resolution, learning (artificial intelligence), neural nets, GAN based model, Feeds BibRef

Schwettmann, S.[Sarah], Hernandez, E.[Evan], Bau, D.[David], Klein, S.[Samuel], Andreas, J.[Jacob], Torralba, A.B.[Antonio B.],
Toward a Visual Concept Vocabulary for GAN Latent Space,
ICCV21(6784-6792)
IEEE DOI 2203
Vocabulary, Visualization, Annotations, Buildings, Natural languages, Transforms, Observers, Neural generative models, BibRef

Lin, C.H., Chang, C., Chen, Y., Juan, D., Wei, W., Chen, H.,
COCO-GAN: Generation by Parts via Conditional Coordinating,
ICCV19(4511-4520)
IEEE DOI 2004
divide and conquer methods, extrapolation, learning (artificial intelligence), neural nets, COCO-GAN, Task analysis BibRef

Wieluch, S., Schwenker, F.,
Dropout Induced Noise for Co-Creative GAN Systems,
Fashion19(3137-3140)
IEEE DOI 2004
neural nets, dropout induced noise, generative adversarial networks, latent space exploration, neural net BibRef

Al-Rawi, M., Bazazian, D., Valveny, E.,
Can Generative Adversarial Networks Teach Themselves Text Segmentation?,
AIM19(3342-3350)
IEEE DOI 2004
data mining, image segmentation, natural language processing, text analysis, unsupervised learning, scene image, F1 Score BibRef

Liu, H., Gu, X., Samaras, D.,
Wasserstein GAN With Quadratic Transport Cost,
ICCV19(4831-4840)
IEEE DOI 2004
learning (artificial intelligence), neural nets, statistical distributions, Wasserstein GAN, Linear programming BibRef

Feng, Z.[Zeyu], Xu, C.[Chang], Tao, D.C.[Da-Cheng],
Self-Supervised Representation Learning by Rotation Feature Decoupling,
CVPR19(10356-10366).
IEEE DOI 2002
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Xing, X.L.[Xiang-Lei], Gao, R.Q.[Rui-Qi], Han, T.[Tian], Zhu, S.C.[Song-Chun], Wu, Y.N.[Ying Nian],
Deformable Generator Networks: Unsupervised Disentanglement of Appearance and Geometry,
PAMI(44), No. 3, March 2022, pp. 1162-1179.
IEEE DOI 2202
BibRef
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Unsupervised Disentangling of Appearance and Geometry by Deformable Generator Network,
CVPR19(10346-10355).
IEEE DOI 2002
Generators, Deformable models, Data models, Shape, Interpolation, Analytical models, Image color analysis, Unsupervised learning, deformable model. 2 separate generators. BibRef

Liu, S.H.[Shao-Hui], Zhang, X.[Xiao], Wangni, J.Q.[Jian-Qiao], Shi, J.B.[Jian-Bo],
Normalized Diversification,
CVPR19(10298-10307).
IEEE DOI 2002
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Wu, J.Q.[Ji-Qing], Huang, Z.W.[Zhi-Wu], Acharya, D.[Dinesh], Li, W.[Wen], Thoma, J.[Janine], Paudel, D.P.[Danda Pani], Van Gool, L.J.[Luc J.],
Sliced Wasserstein Generative Models,
CVPR19(3708-3717).
IEEE DOI 2002
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Zhao, J.B.J.[Jun-Bo Jake], Cho, K.H.[Kyung-Hyun],
Retrieval-Augmented Convolutional Neural Networks Against Adversarial Examples,
CVPR19(11555-11563).
IEEE DOI 2002
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Yu, B.[Bing], Wu, J.F.[Jing-Feng], Ma, J.W.[Jin-Wen], Zhu, Z.X.[Zhan-Xing],
Tangent-Normal Adversarial Regularization for Semi-Supervised Learning,
CVPR19(10668-10676).
IEEE DOI 2002
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Jaiswal, A.[Ayush], Wu, Y.[Yue], Abd Almageed, W.[Wael], Masi, I.[Iacopo], Natarajan, P.[Premkumar],
AIRD: Adversarial Learning Framework for Image Repurposing Detection,
CVPR19(11322-11331).
IEEE DOI 2002
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Taghanaki, S.A.[Saeid Asgari], Abhishek, K.[Kumar], Azizi, S.[Shekoofeh], Hamarneh, G.[Ghassan],
A Kernelized Manifold Mapping to Diminish the Effect of Adversarial Perturbations,
CVPR19(11332-11341).
IEEE DOI 2002
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Liang, J.[Jian], Cao, Y.[Yuren], Zhang, C.B.[Chen-Bin], Chang, S.Y.[Shi-Yu], Bai, K.[Kun], Xu, Z.L.[Zeng-Lin],
Additive Adversarial Learning for Unbiased Authentication,
CVPR19(11420-11429).
IEEE DOI 2002
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Liu, Z.H.[Zi-Hao], Liu, Q.[Qi], Liu, T.[Tao], Xu, N.[Nuo], Lin, X.[Xue], Wang, Y.Z.[Yan-Zhi], Wen, W.J.[Wu-Jie],
Feature Distillation: DNN-Oriented JPEG Compression Against Adversarial Examples,
CVPR19(860-868).
IEEE DOI 2002
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Huh, M.Y.[Min-Young], Sun, S.H.[Shao-Hua], Zhang, N.[Ning],
Feedback Adversarial Learning: Spatial Feedback for Improving Generative Adversarial Networks,
CVPR19(1476-1485).
IEEE DOI 2002
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Ghasedi, K.[Kamran], Wang, X.Q.[Xiao-Qian], Deng, C.[Cheng], Huang, H.[Heng],
Balanced Self-Paced Learning for Generative Adversarial Clustering Network,
CVPR19(4386-4395).
IEEE DOI 2002
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Qi, M.S.[Meng-Shi], Wang, Y.H.[Yun-Hong], Qin, J.[Jie], Li, A.[Annan],
KE-GAN: Knowledge Embedded Generative Adversarial Networks for Semi-Supervised Scene Parsing,
CVPR19(5232-5241).
IEEE DOI 2002
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Park, J.S.[Jae Sung], Rohrbach, M.[Marcus], Darrell, T.J.[Trevor J.], Rohrbach, A.[Anna],
Adversarial Inference for Multi-Sentence Video Description,
CVPR19(6591-6601).
IEEE DOI 2002
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Xiao, C.W.[Chao-Wei], Yang, D.W.[Da-Wei], Li, B.[Bo], Deng, J.[Jia], Liu, M.Y.[Ming-Yan],
MeshAdv: Adversarial Meshes for Visual Recognition,
CVPR19(6891-6900).
IEEE DOI 2002
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Inkawhich, N.[Nathan], Wen, W.[Wei], Li, H.(.[Hai (Helen)], Chen, Y.R.[Yi-Ran],
Feature Space Perturbations Yield More Transferable Adversarial Examples,
CVPR19(7059-7067).
IEEE DOI 2002
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Heim, E.[Eric],
Constrained Generative Adversarial Networks for Interactive Image Generation,
CVPR19(10745-10753).
IEEE DOI 2002
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Gomez-Villa, A.[Alexander], Martin, A.[Adrian], Vazquez-Corral, J.[Javier], Bertalmio, M.[Marcelo],
Convolutional Neural Networks Can Be Deceived by Visual Illusions,
CVPR19(12301-12309).
IEEE DOI 2002
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Liu, F.[Fang], Deng, X.M.[Xiao-Ming], Lai, Y.K.[Yu-Kun], Liu, Y.J.[Yong-Jin], Ma, C.X.[Cui-Xia], Wang, H.A.[Hong-An],
SketchGAN: Joint Sketch Completion and Recognition With Generative Adversarial Network,
CVPR19(5823-5832).
IEEE DOI 2002
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Eghbal-zadeh, H.[Hamid], Zellinger, W.[Werner], Widmer, G.[Gerhard],
Mixture Density Generative Adversarial Networks,
CVPR19(5813-5822).
IEEE DOI 2002
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Zhu, B.[Bin], Ngo, C.W.[Chong-Wah], Chen, J.J.[Jing-Jing], Hao, Y.B.[Yan-Bin],
R2GAN: Cross-Modal Recipe Retrieval With Generative Adversarial Network,
CVPR19(11469-11478).
IEEE DOI 2002
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Romijnders, R.[Rob], Mahendran, A.[Aravindh], Tschannen, M.[Michael], Djolonga, J.[Josip], Ritter, M.[Marvin], Houlsby, N.[Neil], Lucic, M.[Mario],
Representation learning from videos in-the-wild: An object-centric approach,
WACV21(177-187)
IEEE DOI 2106
Visualization, Transfer learning, Detectors, Image representation, Benchmark testing BibRef

Chen, T.[Ting], Zhai, X.H.[Xiao-Hua], Ritter, M.[Marvin], Lucic, M.[Mario], Houlsby, N.[Neil],
Self-Supervised GANs via Auxiliary Rotation Loss,
CVPR19(12146-12155).
IEEE DOI 2002
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Papadopoulos, D.P.[Dim P.], Tamaazousti, Y.[Youssef], Ofli, F.[Ferda], Weber, I.[Ingmar], Torralba, A.B.[Antonio B.],
How to Make a Pizza: Learning a Compositional Layer-Based GAN Model,
CVPR19(7994-8003).
IEEE DOI 2002
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Deshpande, I.[Ishan], Hu, Y.T.[Yuan-Ting], Sun, R.[Ruoyu], Pyrros, A.[Ayis], Siddiqui, N.[Nasir], Koyejo, S.[Sanmi], Zhao, Z.Z.[Zhi-Zhen], Forsyth, D.A.[David A.], Schwing, A.G.[Alexander G.],
Max-Sliced Wasserstein Distance and Its Use for GANs,
CVPR19(10640-10648).
IEEE DOI 2002
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Vandenhende, S., de Brabandere, B., Neven, D., Van Gool, L.J.,
A Three-Player GAN: Generating Hard Samples to Improve Classification Networks,
MVA19(1-6)
DOI Link 1806
game theory, image classification, image recognition, learning (artificial intelligence), Computational modeling BibRef

Pinetz, T.[Thomas], Soukup, D.[Daniel], Pock, T.[Thomas],
On the Estimation of the Wasserstein Distance in Generative Models,
GCPR19(156-170).
Springer DOI 1911
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Kim, B., Lee, J., Kim, K., Kim, S., Kim, J.,
Collaborative Method for Incremental Learning on Classification and Generation,
ICIP19(390-394)
IEEE DOI 1910
Incremental Learning, Deep Neural Networks, Generative Adversarial Networks BibRef

Mao, Q., Wang, S., Zhang, X., Wang, S., Ma, S.,
Fidelity or Quality? A Region-Aware Framework for Enhanced Image Decoding via Hybrid Neural Networks,
ICIP19(2616-2620)
IEEE DOI 1910
Image restoration, generative adversarial networks, enhanced image decoding, perceptual quality, fidelity BibRef

Zhuang, Y., Hsu, C.,
Detecting Generated Image Based on a Coupled Network with Two-Step Pairwise Learning,
ICIP19(3212-3216)
IEEE DOI 1910
Forgery detection, generative adversarial networks, triplet loss, deep learning, coupled network BibRef

Lu, Y., Velipasalar, S.,
Autonomous Choice of Deep Neural Network Parameters by a Modified Generative Adversarial Network,
ICIP19(3846-3850)
IEEE DOI 1910
Deep learning, neural networks, parameter choice, generative adversarial networks BibRef

Yang, R., Nakayama, H.,
Bipolar GAN: Double Check the Solution Space and Lighten False Positive Errors in Generative Adversarial Nets,
ICIP19(4260-4264)
IEEE DOI 1910
GAN, Bipolar discriminator, Compatibility BibRef

Wang, Y., Nikkhah, M., Zhu, X., Tan, W., Liston, R.,
Learning Geographically Distributed Data for Multiple Tasks Using Generative Adversarial Networks,
ICIP19(4589-4593)
IEEE DOI 1910
distributed machine learning, generative adversarial networks (GAN), semi-supervised learning BibRef

Caldelli, R., Becarelli, R., Carrara, F., Falchi, F., Amato, G.,
Exploiting CNN Layer Activations to Improve Adversarial Image Classification,
ICIP19(2289-2293)
IEEE DOI 1910
Adversarial images, neural networks, layer activations, adversarial detection BibRef

Guo, W.K.[Wei-Kuo], Liang, J.[Jian], Kong, X.W.[Xiang-Wei], Song, L.X.[Ling-Xiao], He, R.[Ran],
X-GACMN: An X-Shaped Generative Adversarial Cross-Modal Network with Hypersphere Embedding,
ACCV18(V:513-529).
Springer DOI 1906
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Ying, G.H.[Guo-Hao], Zou, Y.T.[Ying-Tian], Wan, L.[Lin], Hu, Y.M.[Yi-Ming], Feng, J.S.[Jia-Shi],
Better Guider Predicts Future Better: Difference Guided Generative Adversarial Networks,
ACCV18(VI:277-292).
Springer DOI 1906
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Heljakka, A.[Ari], Solin, A.[Arno], Kannala, J.H.[Ju-Ho],
Pioneer Networks: Progressively Growing Generative Autoencoder,
ACCV18(I:22-38).
Springer DOI 1906
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Alberti, M.[Michele], Pondenkandath, V.[Vinaychandran], Würsch, M.[Marcel], Bouillon, M.[Manuel], Seuret, M.[Mathias], Ingold, R.[Rolf], Liwicki, M.[Marcus],
Are You Tampering with My Data?,
Objectionable18(II:296-312).
Springer DOI 1905
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Belagiannis, V.[Vasileios], Farshad, A.[Azade], Galasso, F.[Fabio],
Adversarial Network Compression,
CEFR-LCV18(IV:431-449).
Springer DOI 1905
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Öngün, C.[Cihan], Temizel, A.[Alptekin],
Paired 3D Model Generation with Conditional Generative Adversarial Networks,
3D-Wild18(I:473-487).
Springer DOI 1905
BibRef

Carrara, F.[Fabio], Becarelli, R.[Rudy], Caldelli, R.[Roberto], Falchi, F.[Fabrizio], Amato, G.[Giuseppe],
Adversarial Examples Detection in Features Distance Spaces,
Objectionable18(II:313-327).
Springer DOI 1905
BibRef

Assens, M.[Marc], Giro-i-Nieto, X.[Xavier], McGuinness, K.[Kevin], O'Connor, N.E.[Noel E.],
PathGAN: Visual Scanpath Prediction with Generative Adversarial Networks,
Egocentric18(V:406-422).
Springer DOI 1905
BibRef

Jaiswal, A.[Ayush], Abd-Almageed, W.[Wael], Wu, Y.[Yue], Natarajan, P.[Premkumar],
Bidirectional Conditional Generative Adversarial Networks,
ACCV18(III:216-232).
Springer DOI 1906
BibRef
Earlier:
CapsuleGAN: Generative Adversarial Capsule Network,
BrainDriven18(III:526-535).
Springer DOI 1905
BibRef

Blum, O.[Oliver], Brattoli, B.[Biagio], Ommer, B.[Björn],
X-GAN: Improving Generative Adversarial Networks with ConveX Combinations,
GCPR18(199-214).
Springer DOI 1905
BibRef

Kazemi, H., Iranmanesh, S.M., Nasrabadi, N.,
Style and Content Disentanglement in Generative Adversarial Networks,
WACV19(848-856)
IEEE DOI 1904
feature extraction, geophysical image processing, image classification, image representation, image texture, Task analysis BibRef

Han, T., Lu, Y., Wu, J., Xing, X., Wu, Y.N.,
Learning Generator Networks for Dynamic Patterns,
WACV19(809-818)
IEEE DOI 1904
convolutional neural nets, image representation, image sequences, learning (artificial intelligence), spatiotemporal phenomena, Dynamics BibRef

Juefei-Xu, F., Boddeti, V.N., Savvides, M.,
Perturbative Neural Networks,
CVPR18(3310-3318)
IEEE DOI 1812
Perturbation methods, Convolution, Standards, Task analysis, Convolutional neural networks, Visualization BibRef

Oseledets, I., Khrulkov, V.,
Art of Singular Vectors and Universal Adversarial Perturbations,
CVPR18(8562-8570)
IEEE DOI 1812
Perturbation methods, Jacobian matrices, Optimization, Neural networks, Visualization, Correlation BibRef

Gupta, A., Johnson, J., Fei-Fei, L., Savarese, S., Alahi, A.,
Social GAN: Socially Acceptable Trajectories with Generative Adversarial Networks,
CVPR18(2255-2264)
IEEE DOI 1812
Trajectory, Computational modeling, Predictive models, Generators, History, Decoding BibRef

Hayes, J.,
On Visible Adversarial Perturbations & Digital Watermarking,
PRIV18(1678-16787)
IEEE DOI 1812
Perturbation methods, Watermarking, Computational modeling, Visualization, Task analysis, Image restoration BibRef

Pal, A., Balasubramanian, V.N.,
Adversarial Data Programming: Using GANs to Relax the Bottleneck of Curated Labeled Data,
CVPR18(1556-1565)
IEEE DOI 1812
Labeling, Data models, Generative adversarial networks, Computational modeling, Programming BibRef

Ma, S., Fu, J., Chen, C.W., Mei, T.,
DA-GAN: Instance-Level Image Translation by Deep Attention Generative Adversarial Networks,
CVPR18(5657-5666)
IEEE DOI 1812
Task analysis, Semantics, Generative adversarial networks, Birds, Geometry BibRef

Hosseini, H., Poovendran, R.,
Semantic Adversarial Examples,
PRIV18(1695-16955)
IEEE DOI 1812
Image color analysis, Perturbation methods, Semantics, Shape, Security, Automobiles, Marine vehicles BibRef

Wu, K., Zhang, C.,
Deep Generative Adversarial Networks for the Sparse Signal Denoising,
ICPR18(1127-1132)
IEEE DOI 1812
Noise reduction, Encoding, Task analysis, Data models, Generative adversarial networks BibRef

Guo, Y.[Ye], Liu, K.[Ke], Yu, Z.Y.[Ze-Yun],
Porous Structure Design in Tissue Engineering Using Anisotropic Radial Basis Functions,
ISVC18(79-90).
Springer DOI 1811
BibRef

Makkapati, V.V.[Vishnu Vardhan], Patro, A., (2017)
Enhancing Symmetry in GAN Generated Fashion Images,
SGAI17(xx-yy).
Springer DOI LNCS 10630. 1811
BibRef

Patro, A., Makkapati, V.V.[Vishnu Vardhan], Mukhopadhyay, J.,
Evaluation of Loss Functions for Estimation of Latent Vectors from GAN,
MLSP18(1-6).
IEEE DOI 1811
BibRef

Tran, N.T.[Ngoc-Trung], Bui, T.A.[Tuan-Anh], Cheung, N.M.[Ngai-Man],
Dist-GAN: An Improved GAN Using Distance Constraints,
ECCV18(XIV: 387-401).
Springer DOI 1810
BibRef

Zhao, B.[Bo], Chang, B.[Bo], Jie, Z.Q.[Ze-Qun], Sigal, L.[Leonid],
Modular Generative Adversarial Networks,
ECCV18(XIV: 157-173).
Springer DOI 1810
BibRef

Zhou, W.[Wen], Hou, X.[Xin], Chen, Y.J.[Yong-Jun], Tang, M.Y.[Meng-Yun], Huang, X.Q.[Xiang-Qi], Gan, X.[Xiang], Yang, Y.[Yong],
Transferable Adversarial Perturbations,
ECCV18(XIV: 471-486).
Springer DOI 1810
BibRef

Jha, A.H.[Ananya Harsh], Anand, S.[Saket], Singh, M.[Maneesh], Veeravasarapu, V.S.R.,
Disentangling Factors of Variation with Cycle-Consistent Variational Auto-encoders,
ECCV18(III: 829-845).
Springer DOI 1810
BibRef

Edraki, M.[Marzieh], Qi, G.J.[Guo-Jun],
Generalized Loss-Sensitive Adversarial Learning with Manifold Margins,
ECCV18(VI: 90-104).
Springer DOI 1810
BibRef

Chang, C.C.[Chia-Che], Lin, C.H.[Chieh Hubert], Lee, C.R.[Che-Rung], Juan, D.C.[Da-Cheng], Wei, W.[Wei], Chen, H.T.[Hwann-Tzong],
Escaping from Collapsing Modes in a Constrained Space,
ECCV18(VII: 212-227).
Springer DOI 1810
mode collapse issue in GANs BibRef

Shmelkov, K.[Konstantin], Schmid, C.[Cordelia], Alahari, K.[Karteek],
How Good Is My GAN?,
ECCV18(II: 218-234).
Springer DOI 1810
BibRef

Liang, X.D.[Xiao-Dan], Zhang, H.[Hao], Lin, L.[Liang], Xing, E.[Eric],
Generative Semantic Manipulation with Mask-Contrasting GAN,
ECCV18(XIII: 574-590).
Springer DOI 1810
BibRef

Wu, J.Q.[Ji-Qing], Huang, Z.W.[Zhi-Wu], Thoma, J.[Janine], Acharya, D.[Dinesh], Van Gool, L.J.[Luc J.],
Wasserstein Divergence for GANs,
ECCV18(VI: 673-688).
Springer DOI 1810
BibRef

Xu, K.[Kai], Zhang, Z.K.[Zhi-Kang], Ren, F.B.[Feng-Bo],
LAPRAN: A Scalable Laplacian Pyramid Reconstructive Adversarial Network for Flexible Compressive Sensing Reconstruction,
ECCV18(X: 491-507).
Springer DOI 1810
BibRef

Zhang, X.[Xi], Lai, H.J.[Han-Jiang], Feng, J.S.[Jia-Shi],
Attention-Aware Deep Adversarial Hashing for Cross-Modal Retrieval,
ECCV18(XV: 614-629).
Springer DOI 1810
BibRef

Liang, J.[Jie], Yang, J.F.[Ju-Feng], Lee, H.Y.[Hsin-Ying], Wang, K.[Kai], Yang, M.H.[Ming-Hsuan],
Sub-GAN: An Unsupervised Generative Model via Subspaces,
ECCV18(XI: 726-743).
Springer DOI 1810
BibRef

Wang, G.[Guan'an], Hu, Q.[Qinghao], Cheng, J.[Jian], Hou, Z.G.[Zeng-Guang],
Semi-supervised Generative Adversarial Hashing for Image Retrieval,
ECCV18(XV: 491-507).
Springer DOI 1810
BibRef

Di, X., Yu, P.,
Multiplicative Noise Channel in Generative Adversarial Networks,
CEFR-LCV17(1165-1172)
IEEE DOI 1802
Additive noise, Additives, Convergence, Gaussian noise, Uncertainty, Visualization BibRef

Metzen, J.H.[Jan Hendrik], Kumar, M.C.[Mummadi Chaithanya], Brox, T.[Thomas], Fischer, V.[Volker],
Universal Adversarial Perturbations Against Semantic Image Segmentation,
ICCV17(2774-2783)
IEEE DOI 1802
Noise specifically generated to fool the system. image denoising, image segmentation, learning (artificial intelligence), arbitrary inputs, BibRef

Chapter on Pattern Recognition, Clustering, Statistics, Grammars, Learning, Neural Nets, Genetic Algorithms continues in
Training of Adversarial Networks .