14.5.8.8 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.[Yiwen], 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

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

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

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

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

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

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

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

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

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

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

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.[Fumin], 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.[Yangbin], Bai, L.[Lu], Hu, Y.Q.[Yi-Qun], Wu, M.[Meihong], 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

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

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, Q.[Qinfeng], 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.[Wenming], 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

Sim, B.[Byeongsu], Oh, G.[Gyutaek], Kim, J.[Jeongsol], Jung, C.Y.[Chan-Yong], Ye, J.C.[Jong Chul],
Optimal Transport Driven CycleGAN for Unsupervised Learning in Inverse Problems,
SIIMS(13), No. 4, 2020, pp. 2281-2306.
DOI Link 2012
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, Computer architecture, 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, Computer vision, 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

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

Zhang, M.[Man], Zhou, Y.[Yong], Zhao, J.[Jiaqi], Xia, S.X.[Shi-Xiong], Wang, J.[Jiaqi], Huang, Z.[Zizheng],
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.[Liangli], Wang, W.[Wei], Peng, D.[Dezhong],
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

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

Gnanha, A.T.[Aurele Tohokantche], Cao, W.[Wenming], 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

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

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

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

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

Zheng, W.[Wenbo], 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
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

Katsumata, K.[Kai], Kobayashi, R.[Ryoga],
Uncertainty Estimates in Deep Generative Models Using Gaussian Processes,
ISVC20(I:121-132).
Springer DOI 2103
BibRef

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
BibRef

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

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

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
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

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
BibRef

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
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

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

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

Peebles, W.[William], Peebles, J.[John], Zhu, J.Y.[Jun-Yan], Efros, A.[Alexei], Torralba, A.[Antonio],
The Hessian Penalty: A Weak Prior for Unsupervised Disentanglement,
ECCV20(VI:581-597).
Springer DOI 2011
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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
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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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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, Computer architecture, 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

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

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

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

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

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

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

Zhang, B., Li, L., Yang, S., Wang, S., Zha, Z., Huang, Q.,
State-Relabeling Adversarial Active Learning,
CVPR20(8753-8762)
IEEE DOI 2008
Task analysis, Uncertainty, Generators, Data models, Computational modeling, Image reconstruction, Learning systems 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

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

Li, M., Lin, J., Ding, Y., Liu, Z., Zhu, J., Han, S.,
GAN Compression: Efficient Architectures for Interactive Conditional GANs,
CVPR20(5283-5293)
IEEE DOI 2008
Generators, Training, Computational modeling, Computer architecture, Generative adversarial networks, Image coding 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

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, Computer vision, 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
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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

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, D.W.[Dong-Wook], Park, S.W.[Sung Woo], Kwon, J.[Junseok],
3D Point Cloud Generative Adversarial Network Based on Tree Structured Graph Convolutions,
ICCV19(3858-3867)
IEEE DOI 2004
Generate 3D data. feature extraction, image classification, image matching, object detection, trees (mathematics), 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.,
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

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

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
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

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

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
computer vision, 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
computer vision, 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], Han, T.[Tian], Gao, R.[Ruiqi], Zhu, S.C.[Song-Chun], Wu, Y.N.[Ying Nian],
Unsupervised Disentangling of Appearance and Geometry by Deformable Generator Network,
CVPR19(10346-10355).
IEEE DOI 2002
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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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
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Jenni, S.[Simon], Favaro, P.[Paolo],
On Stabilizing Generative Adversarial Training With Noise,
CVPR19(12137-12145).
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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Park, S.W.[Sung Woo], Kwon, J.[Junseok],
Sphere Generative Adversarial Network Based on Geometric Moment Matching,
CVPR19(4287-4296).
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.[Chaowei], Yang, D.[Dawei], Li, B.[Bo], Deng, J.[Jia], Liu, M.Y.[Ming-Yan],
MeshAdv: Adversarial Meshes for Visual Recognition,
CVPR19(6891-6900).
IEEE DOI 2002
BibRef

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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Avraham, G.[Gil], Zuo, Y.[Yan], Drummond, T.[Tom],
Parallel Optimal Transport GAN,
CVPR19(4406-4415).
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.[Cuixia], 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.[Yanbin],
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.[Antonio],
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.[David], Schwing, A.G.[Alexander G.],
Max-Sliced Wasserstein Distance and Its Use for GANs,
CVPR19(10640-10648).
IEEE DOI 2002
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Horiuchi, Y.[Yusuke], Iizuka, S.[Satoshi], Simo-Serra, E.[Edgar], Ishikawa, H.[Hiroshi],
Spectral Normalization and Relativistic Adversarial Training for Conditional Pose Generation with Self-Attention,
MVA19(1-5)
DOI Link 1911
image resolution, learning (artificial intelligence), pose estimation, spectral normalization, Fading channels BibRef

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
BibRef

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

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, Computer vision, 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

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

Zhang, C.[Chao], Yang, F.[Fei], Qiu, G.P.[Guo-Ping], Zhang, Q.[Qian],
Salient Object Detection With Capsule-Based Conditional Generative Adversarial Network,
ICIP19(81-85)
IEEE DOI 1910
Salient Object Detection, Image-level Saliency, Generative Adversarial Network, cGAN, Capsule Net 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

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

Ying, G.H.[Guo-Hao], Zou, Y.T.[Ying-Tian], Wan, L.[Lin], Hu, Y.M.[Yi-Ming], Feng, J.[Jiashi],
Better Guider Predicts Future Better: Difference Guided Generative Adversarial Networks,
ACCV18(VI:277-292).
Springer DOI 1906
BibRef

Heljakka, A.[Ari], Solin, A.[Arno], Kannala, J.H.[Ju-Ho],
Pioneer Networks: Progressively Growing Generative Autoencoder,
ACCV18(I:22-38).
Springer DOI 1906
BibRef

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
BibRef

Belagiannis, V.[Vasileios], Farshad, A.[Azade], Galasso, F.[Fabio],
Adversarial Network Compression,
CEFR-LCV18(IV:431-449).
Springer DOI 1905
BibRef

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

Zhang, W., Liu, Y., Dong, C., Qiao, Y.,
RankSRGAN: Generative Adversarial Networks With Ranker for Image Super-Resolution,
ICCV19(3096-3105)
IEEE DOI 2004

WWW Link. 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.[Yihao], 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

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

Shao, H.[Hang], Kumar, A.[Abhishek], Fletcher, P.T.[P. Thomas],
The Riemannian Geometry of Deep Generative Models,
Diff-CVML18(428-4288)
IEEE DOI 1812
Manifolds, Jacobian matrices, Computational modeling, Measurement, Geometry, Generators, Data models 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

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

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

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, Computer vision, 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

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

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

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

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

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

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

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

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

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.[Zequn], 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

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

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

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

Xu, K.[Kai], Zhang, Z.[Zhikang], Ren, F.[Fengbo],
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

Wu, Z.Y.[Zhen-Yu], Wang, Z.Y.[Zhang-Yang], Wang, Z.W.[Zhao-Wen], Jin, H.L.[Hai-Lin],
Towards Privacy-Preserving Visual Recognition via Adversarial Training: A Pilot Study,
ECCV18(XVI: 627-645).
Springer DOI
WWW Link. 1810

See also Privacy-Preserving Visual Recognition PA-HMDB51. 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

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

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

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

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 .