Tao, Y.T.[Yi-Ting],
Xu, M.Z.[Miao-Zhong],
Zhang, F.[Fan],
Du, B.[Bo],
Zhang, L.P.[Liang-Pei],
Unsupervised-Restricted Deconvolutional Neural Network for Very High
Resolution Remote-Sensing Image Classification,
GeoRS(55), No. 12, December 2017, pp. 6805-6823.
IEEE DOI
1712
Use small number of labeled pixels.
Data models, Deconvolution, Feature extraction, Image resolution,
Remote sensing, Satellites, Training,
very high resolution (VHR) image per-pixel classification
BibRef
Hu, F.[Fan],
Xia, G.S.[Gui-Song],
Hu, J.W.[Jing-Wen],
Zhang, L.P.[Liang-Pei],
Transferring Deep Convolutional Neural Networks for the Scene
Classification of High-Resolution Remote Sensing Imagery,
RS(7), No. 11, 2015, pp. 14680.
DOI Link
1512
BibRef
Tao, Y.T.[Yi-Ting],
Xu, M.Z.[Miao-Zhong],
Zhong, Y.F.[Yan-Fei],
Cheng, Y.F.[Yu-Feng],
GAN-Assisted Two-Stream Neural Network for High-Resolution Remote
Sensing Image Classification,
RS(9), No. 12, 2017, pp. xx-yy.
DOI Link
1802
BibRef
Xu, R.D.[Ru-Dong],
Tao, Y.T.[Yi-Ting],
Lu, Z.Y.[Zhong-Yuan],
Zhong, Y.F.[Yan-Fei],
Attention-Mechanism-Containing Neural Networks for High-Resolution
Remote Sensing Image Classification,
RS(10), No. 10, 2018, pp. xx-yy.
DOI Link
1811
BibRef
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
Biggio, B.[Battista],
Roli, F.[Fabio],
Wild patterns: Ten years after the rise of adversarial machine
learning,
PR(84), 2018, pp. 317-331.
Elsevier DOI
1809
Adversarial machine learning, Evasion attacks,
Poisoning attacks, Adversarial examples, Secure learning, Deep learning
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
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
Long, M.S.[Ming-Sheng],
Cao, Y.[Yue],
Cao, Z.J.[Zhang-Jie],
Wang, J.M.[Jian-Min],
Jordan, M.I.[Michael I.],
Transferable Representation Learning with Deep Adaptation Networks,
PAMI(41), No. 12, December 2019, pp. 3071-3085.
IEEE DOI
1911
BibRef
Earlier: A3, A1, A4, A5, Only:
Partial Transfer Learning with Selective Adversarial Networks,
CVPR18(2724-2732)
IEEE DOI
1812
Task analysis, Learning systems, Adaptation models,
Convolutional neural networks, Deep learning, Domain adaptation,
multiple kernel learning.
Feature extraction, Task analysis, Standards, Big Data, Bridges,
Training, Labeling
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
Hang, J.[Jie],
Han, K.[Keji],
Chen, H.[Hui],
Li, Y.[Yun],
Ensemble adversarial black-box attacks against deep learning systems,
PR(101), 2020, pp. 107184.
Elsevier DOI
2003
Black-box attack, Vulnerability, Ensemble adversarial attack,
Diversity, Transferability
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
Zhang, Y.G.[Yong-Gang],
Tian, X.M.[Xin-Mei],
Li, Y.[Ya],
Wang, X.C.[Xin-Chao],
Tao, D.C.[Da-Cheng],
Principal Component Adversarial Example,
IP(29), 2020, pp. 4804-4815.
IEEE DOI
2003
Manifolds, Neural networks, Perturbation methods, Distortion,
Task analysis, Robustness, Principal component analysis,
manifold learning
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
Croce, F.[Francesco],
Rauber, J.[Jonas],
Hein, M.[Matthias],
Scaling up the Randomized Gradient-Free Adversarial Attack Reveals
Overestimation of Robustness Using Established Attacks,
IJCV(128), No. 4, April 2020, pp. 1028-1046.
Springer DOI
2004
BibRef
Earlier: A1, A3, Only:
A Randomized Gradient-Free Attack on ReLU Networks,
GCPR18(215-227).
Springer DOI
1905
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
Romano, Y.[Yaniv],
Aberdam, A.[Aviad],
Sulam, J.[Jeremias],
Elad, M.[Michael],
Adversarial Noise Attacks of Deep Learning Architectures:
Stability Analysis via Sparse-Modeled Signals,
JMIV(62), No. 3, April 2020, pp. 313-327.
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.[Jieyu],
Ye, X.[Xulun],
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
Serban, A.[Alex],
Poll, E.[Erik],
Visser, J.[Joost],
Adversarial Examples on Object Recognition: A Comprehensive Survey,
Surveys(53), No. 3, June 2020, pp. xx-yy.
DOI Link
2007
Survey, Adversairal Networks. security, robustness, machine learning, Adversarial examples
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
Goodfellow, I.[Ian],
Pouget-Abadie, J.[Jean],
Mirza, M.[Mehdi],
Xu, B.[Bing],
Warde-Farley, D.[David],
Ozair, S.[Sherjil],
Courville, A.[Aaron],
Bengio, Y.[Yoshua],
Generative Adversarial Networks,
CACM(63), No. 11, November 2020, pp. 139-144.
DOI Link
2010
Survey, GAN.
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
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
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
BibRef
Liu, Y.F.[Yun-Fei],
Ma, X.J.[Xing-Jun],
Bailey, J.[James],
Lu, F.[Feng],
Reflection Backdoor: A Natural Backdoor Attack on Deep Neural Networks,
ECCV20(X:182-199).
Springer DOI
2011
BibRef
Qin, Y.P.[Yi-Peng],
Mitra, N.[Niloy],
Wonka, P.[Peter],
How Does Lipschitz Regularization Influence GAN Training?,
ECCV20(XVI: 310-326).
Springer DOI
2010
BibRef
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
BibRef
Feng, X.J.[Xin-Jie],
Yao, H.X.[Hong-Xun],
Che, W.B.[Wen-Bin],
Zhang, S.P.[Sheng-Ping],
An Effective Way to Boost Black-box Adversarial Attack,
MMMod20(I:393-404).
Springer DOI
2003
BibRef
Williams, F.[Francis],
Parent-Lévesque, J.[Jérôme],
Nowrouzezahrai, D.[Derek],
Panozzo, D.[Daniele],
Yi, K.M.[Kwang Moo],
Tagliasacchi, A.[Andrea],
VoronoiNet: General Functional Approximators with Local Support,
L3DGM20(1069-1073)
IEEE DOI
2008
Shape, Decoding, Image reconstruction,
Training, 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
Xia, H.,
Ding, Z.,
Structure Preserving Generative Cross-Domain Learning,
CVPR20(4363-4372)
IEEE DOI
2008
Feature extraction, Training, Measurement, Robustness,
Adaptation models, Neural networks, Task analysis
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
Zheng, S.,
Zhu, Z.,
Zhang, X.,
Liu, Z.,
Cheng, J.,
Zhao, Y.,
Distribution-Induced Bidirectional Generative Adversarial Network for
Graph Representation Learning,
CVPR20(7222-7231)
IEEE DOI
2008
Generative adversarial networks, Robustness, Data models,
Generators, Task analysis, Gaussian distribution
BibRef
Costales, R.,
Mao, C.,
Norwitz, R.,
Kim, B.,
Yang, J.,
Live Trojan Attacks on Deep Neural Networks,
AML-CV20(3460-3469)
IEEE DOI
2008
Trojan horses, Computational modeling, Neural networks,
Machine learning
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
Vivek, B.S.,
Revanur, A.[Ambareesh],
Venkat, N.[Naveen],
Babu, R.V.[R. Venkatesh],
Plug-And-Pipeline: Efficient Regularization for Single-Step
Adversarial Training,
TCV20(138-146)
IEEE DOI
2008
Training, Robustness, Computational modeling, Perturbation methods,
Iterative methods, Backpropagation, Data models
BibRef
Zhang, C.,
Benz, P.,
Imtiaz, T.,
Kweon, I.S.,
Understanding Adversarial Examples From the Mutual Influence of
Images and Perturbations,
CVPR20(14509-14518)
IEEE DOI
2008
Perturbation methods, Correlation, Training data,
Feature extraction, Training, Task analysis, Robustness
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
Haque, M.,
Chauhan, A.,
Liu, C.,
Yang, W.,
ILFO: Adversarial Attack on Adaptive Neural Networks,
CVPR20(14252-14261)
IEEE DOI
2008
Computational modeling, Energy consumption, Robustness,
Neural networks, Adaptation models, Machine learning, Perturbation methods
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
Zheng, H.,
Zhang, Z.,
Gu, J.,
Lee, H.,
Prakash, A.,
Efficient Adversarial Training With Transferable Adversarial Examples,
CVPR20(1178-1187)
IEEE DOI
2008
Training, Perturbation methods, Robustness, Computational modeling,
Measurement, Iterative methods, Silicon
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
Shi, Y.,
Han, Y.,
Tian, Q.,
Polishing Decision-Based Adversarial Noise With a Customized Sampling,
CVPR20(1027-1035)
IEEE DOI
2008
Gaussian distribution, Sensitivity, Noise reduction, Optimization,
Image coding, Robustness, Standards
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
Xie, C.,
Tan, M.,
Gong, B.,
Wang, J.,
Yuille, A.L.,
Le, Q.V.,
Adversarial Examples Improve Image Recognition,
CVPR20(816-825)
IEEE DOI
2008
Training, Robustness, Degradation, Image recognition,
Perturbation methods, Standards, Supervised learning
BibRef
Zhou, M.,
Wu, J.,
Liu, Y.,
Liu, S.,
Zhu, C.,
DaST: Data-Free Substitute Training for Adversarial Attacks,
CVPR20(231-240)
IEEE DOI
2008
Data models, Training, Machine learning, Perturbation methods,
Task analysis, Estimation
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
Dabouei, A.,
Soleymani, S.,
Taherkhani, F.,
Dawson, J.,
Nasrabadi, N.M.,
SmoothFool: An Efficient Framework for Computing Smooth Adversarial
Perturbations,
WACV20(2654-2663)
IEEE DOI
2006
Perturbation methods, Frequency-domain analysis, Robustness,
Training, Optimization, Network architecture, Topology
BibRef
Brodie, M.,
Rasmussen, B.,
Tensmeyer, C.,
Corbitt, S.,
Martinez, T.,
CoachGAN,
WACV20(3472-3481)
IEEE DOI
2006
Training, Generators, Integrated circuits,
Generative adversarial networks, Optimization, Neural networks
BibRef
Huang, R.,
Xu, W.,
Lee, T.,
Cherian, A.,
Wang, Y.,
Marks, T.K.,
FX-GAN: Self-Supervised GAN Learning via Feature Exchange,
WACV20(3183-3191)
IEEE DOI
2006
Task analysis, Generative adversarial networks,
Generators, Training, Optimization, Games
BibRef
Ganeshan, A.[Aditya],
Vivek, B.S.,
Radhakrishnan, V.B.[Venkatesh Babu],
FDA: Feature Disruptive Attack,
ICCV19(8068-8078)
IEEE DOI
2004
Deal with adversarial attacks.
image classification, image representation,
learning (artificial intelligence), neural nets, optimisation,
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
Han, J.,
Dong, X.,
Zhang, R.,
Chen, D.,
Zhang, W.,
Yu, N.,
Luo, P.,
Wang, X.,
Once a MAN: Towards Multi-Target Attack via Learning Multi-Target
Adversarial Network Once,
ICCV19(5157-5166)
IEEE DOI
2004
convolutional neural nets, learning (artificial intelligence),
pattern classification, security of data, Decoding
BibRef
Brunner, T.,
Diehl, F.,
Le, M.T.,
Knoll, A.,
Guessing Smart:
Biased Sampling for Efficient Black-Box Adversarial Attacks,
ICCV19(4957-4965)
IEEE DOI
2004
application program interfaces, cloud computing,
feature extraction, image classification, security of data, Training
BibRef
Liu, Y.J.[Yu-Jia],
Moosavi-Dezfooli, S.M.[Seyed-Mohsen],
Frossard, P.[Pascal],
A Geometry-Inspired Decision-Based Attack,
ICCV19(4889-4897)
IEEE DOI
2004
Deal with adversarial attack.
geometry, image classification, image recognition, neural nets,
security of data, black-box settings, Gaussian noise
BibRef
Li, J.,
Ji, R.,
Liu, H.,
Hong, X.,
Gao, Y.,
Tian, Q.,
Universal Perturbation Attack Against Image Retrieval,
ICCV19(4898-4907)
IEEE DOI
2004
feature extraction, image classification, image representation,
image retrieval, learning (artificial intelligence), Pipelines
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.[Dongwook],
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
Finlay, C.,
Pooladian, A.,
Oberman, A.,
The LogBarrier Adversarial Attack:
Making Effective Use of Decision Boundary Information,
ICCV19(4861-4869)
IEEE DOI
2004
gradient methods, image classification, minimisation, neural nets,
security of data, LogBarrier adversarial attack, Benchmark testing
BibRef
Huang, Q.,
Katsman, I.,
Gu, Z.,
He, H.,
Belongie, S.,
Lim, S.,
Enhancing Adversarial Example Transferability With an Intermediate
Level Attack,
ICCV19(4732-4741)
IEEE DOI
2004
cryptography, neural nets, optimisation, black-box transferability,
source model, target models, adversarial examples,
Artificial intelligence
BibRef
Croce, F.,
Hein, M.,
Sparse and Imperceivable Adversarial Attacks,
ICCV19(4723-4731)
IEEE DOI
2004
gradient methods, learning (artificial intelligence),
neural nets, pattern classification, security of data, Image edge detection
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
Liu, K.,
Qiu, G.,
Tang, W.,
Zhou, F.,
Spectral Regularization for Combating Mode Collapse in GANs,
ICCV19(6381-6389)
IEEE DOI
2004
neural nets, singular value decomposition, SR-GANs, SN-GANs,
mode collapse problem, spectral normalized GANs, Optimization
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
Che, F.,
Zhu, X.,
Yang, T.,
Yu, T.,
3SGAN: 3D Shape Embedded Generative Adversarial Networks,
AIM19(3305-3314)
IEEE DOI
2004
edge detection, image colour analysis,
learning (artificial intelligence), neural nets, multiview
BibRef
Jandial, S.,
Mangla, P.,
Varshney, S.,
Balasubramanian, V.,
AdvGAN++: Harnessing Latent Layers for Adversary Generation,
NeruArch19(2045-2048)
IEEE DOI
2004
feature extraction, neural nets, MNIST datasets, CIFAR-10 datasets,
attack rates, realistic images, latent features, input image,
AdvGAN
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
Saha, S.,
Kumar, A.,
Sahay, P.,
Jose, G.,
Kruthiventi, S.,
Muralidhara, H.,
Attack Agnostic Statistical Method for Adversarial Detection,
SDL-CV19(798-802)
IEEE DOI
2004
feature extraction, image classification,
learning (artificial intelligence), neural nets, Adversarial Attack
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
Wang, C.L.[Cheng-Long],
Bunel, R.[Rudy],
Dvijotham, K.[Krishnamurthy],
Huang, P.S.[Po-Sen],
Grefenstette, E.[Edward],
Kohli, P.[Pushmeet],
Knowing When to Stop: Evaluation and Verification of Conformity to
Output-Size Specifications,
CVPR19(12252-12261).
IEEE DOI
2002
ulnerability of these models to attacks aimed at changing the output-size.
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
BibRef
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
BibRef
Modas, A.[Apostolos],
Moosavi-Dezfooli, S.M.[Seyed-Mohsen],
Frossard, P.[Pascal],
SparseFool: A Few Pixels Make a Big Difference,
CVPR19(9079-9088).
IEEE DOI
2002
sparse attack.
BibRef
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
BibRef
Liu, X.F.[Xiao-Feng],
Li, S.[Site],
Kong, L.S.[Ling-Sheng],
Xie, W.Q.[Wan-Qing],
Jia, P.[Ping],
You, J.[Jane],
Kumar, B.V.K.,
Feature-Level Frankenstein:
Eliminating Variations for Discriminative Recognition,
CVPR19(637-646).
IEEE DOI
2002
BibRef
Jenni, S.[Simon],
Favaro, P.[Paolo],
On Stabilizing Generative Adversarial Training With Noise,
CVPR19(12137-12145).
IEEE DOI
2002
BibRef
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
BibRef
Yu, B.[Bing],
Wu, J.F.[Jing-Feng],
Ma, J.W.[Jin-Wen],
Zhu, Z.[Zhanxing],
Tangent-Normal Adversarial Regularization for Semi-Supervised Learning,
CVPR19(10668-10676).
IEEE DOI
2002
BibRef
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
BibRef
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,
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2002
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2002
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2002
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2002
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Constrained Generative Adversarial Networks for Interactive Image
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2002
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Convolutional Neural Networks Can Be Deceived by Visual Illusions,
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2002
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Parallel Optimal Transport GAN,
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2002
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Liu, F.[Fang],
Deng, X.M.[Xiao-Ming],
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SketchGAN: Joint Sketch Completion and Recognition With Generative
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IEEE DOI
2002
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IEEE DOI
2002
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Rob-GAN: Generator, Discriminator, and Adversarial Attacker,
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IEEE DOI
2002
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Zhu, B.[Bin],
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R2GAN: Cross-Modal Recipe Retrieval With Generative Adversarial Network,
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IEEE DOI
2002
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Self-Supervised GANs via Auxiliary Rotation Loss,
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IEEE DOI
2002
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How to Make a Pizza: Learning a Compositional Layer-Based GAN Model,
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IEEE DOI
2002
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Zhao, Z.Z.[Zhi-Zhen],
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Max-Sliced Wasserstein Distance and Its Use for GANs,
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IEEE DOI
2002
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Iizuka, S.[Satoshi],
Simo-Serra, E.[Edgar],
Ishikawa, H.[Hiroshi],
Spectral Normalization and Relativistic Adversarial Training for
Conditional Pose Generation with Self-Attention,
MVA19(1-5)
DOI Link
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image resolution, learning (artificial intelligence),
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Vandenhende, S.,
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Neven, D.,
Van Gool, L.J.,
A Three-Player GAN: Generating Hard Samples to Improve Classification
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MVA19(1-6)
DOI Link
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game theory, image classification, image recognition,
learning (artificial intelligence),
Computational modeling
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On the Estimation of the Wasserstein Distance in Generative Models,
GCPR19(156-170).
Springer DOI
1911
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Gupta, P.[Puneet],
Rahtu, E.[Esa],
MLAttack: Fooling Semantic Segmentation Networks by Multi-layer Attacks,
GCPR19(401-413).
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1911
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Lee, J.,
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Collaborative Method for Incremental Learning on Classification and
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ICIP19(390-394)
IEEE DOI
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Incremental Learning, Deep Neural Networks, Generative Adversarial Networks
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Han, B.N.[Bing-Nan],
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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
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Li, W.Y.[Wen-Yue],
Han, B.N.[Bing-Nan],
Hyperspectral Image Classification Based on Generative Adversarial
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ICIP19(405-409)
IEEE DOI
1910
Hyperspectral classification, generative adversarial networks,
spatial semantic information
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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
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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
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Zhang, C.,
Yang, F.,
Qiu, G.,
Zhang, Q.,
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
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Caldelli, R.,
Becarelli, R.,
Carrara, F.,
Falchi, F.,
Amato, G.,
Exploiting CNN Layer Activations to Improve Adversarial Image
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ICIP19(2289-2293)
IEEE DOI
1910
Adversarial images, neural networks, layer activations, adversarial detection
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Barni, M.,
Kallas, K.,
Tondi, B.,
A New Backdoor Attack in CNNS by Training Set Corruption Without
Label Poisoning,
ICIP19(101-105)
IEEE DOI
1910
Adversarial learning, security of deep learning,
backdoor poisoning attacks, training with poisoned data
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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
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Yao, Y.[Yao],
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Unsupervised Transformation Network Based on GANs for Target-Domain
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Springer DOI
1906
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Liang, J.[Jian],
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X-GACMN: An X-Shaped Generative Adversarial Cross-Modal Network with
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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],
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Better Guider Predicts Future Better:
Difference Guided Generative Adversarial Networks,
ACCV18(VI:277-292).
Springer DOI
1906
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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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Pondenkandath, V.[Vinaychandran],
Würsch, M.[Marcel],
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Are You Tampering with My Data?,
Objectionable18(II:296-312).
Springer DOI
1905
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Galasso, F.[Fabio],
Adversarial Network Compression,
CEFR-LCV18(IV:431-449).
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1905
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Paired 3D Model Generation with Conditional Generative Adversarial
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3D-Wild18(I:473-487).
Springer DOI
1905
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Caldelli, R.[Roberto],
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Adversarial Examples Detection in Features Distance Spaces,
Objectionable18(II:313-327).
Springer DOI
1905
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Assens, M.[Marc],
Giro-i-Nieto, X.[Xavier],
McGuinness, K.[Kevin],
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PathGAN: Visual Scanpath Prediction with Generative Adversarial
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Egocentric18(V:406-422).
Springer DOI
1905
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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
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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
Zhao, W.[Wei],
Yang, P.P.[Peng-Peng],
Ni, R.R.[Rong-Rong],
Zhao, Y.[Yao],
Li, W.J.[Wen-Jie],
Cycle GAN-Based Attack on Recaptured Images to Fool both Human and
Machine,
IWDW18(83-92).
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
Wang, S.,
Shi, Y.,
Han, Y.,
Universal Perturbation Generation for Black-box Attack Using
Evolutionary Algorithms,
ICPR18(1277-1282)
IEEE DOI
1812
Perturbation methods, Evolutionary computation, Sociology,
Statistics, Training, Neural networks, Robustness
BibRef
Xu, X.J.[Xiao-Jun],
Chen, X.Y.[Xin-Yun],
Liu, C.[Chang],
Rohrbach, A.[Anna],
Darrell, T.J.[Trevor J.],
Song, D.[Dawn],
Fooling Vision and Language Models Despite Localization and Attention
Mechanism,
CVPR18(4951-4961)
IEEE DOI
1812
Attacks.
Prediction algorithms, Computational modeling, Neural networks,
Knowledge discovery, Visualization, Predictive models, Natural languages
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
Anoosheh, A.,
Agustsson, E.,
Timofte, R.,
Van Gool, L.J.,
ComboGAN: Unrestrained Scalability for Image Domain Translation,
Restoration18(896-8967)
IEEE DOI
1812
Training, Generators, Decoding, Computer vision,
Task analysis, Data models
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
Zhang, J.,
Ding, Z.,
Li, W.,
Ogunbona, P.,
Importance Weighted Adversarial Nets for Partial Domain Adaptation,
CVPR18(8156-8164)
IEEE DOI
1812
Feature extraction, Task analysis, Training, Games, Neural networks
BibRef
Li, H.,
Pan, S.J.,
Wang, S.,
Kot, A.C.,
Domain Generalization with Adversarial Feature Learning,
CVPR18(5400-5409)
IEEE DOI
1812
Data models, Training, Training data, Adaptation models,
Decoding, Predictive models
BibRef
Zhang, W.,
Ouyang, W.,
Li, W.,
Xu, D.,
Collaborative and Adversarial Network for Unsupervised Domain
Adaptation,
CVPR18(3801-3809)
IEEE DOI
1812
Training, Collaboration, Feature extraction, Adaptation models,
Visualization, Task analysis, Computer vision
BibRef
Liu, Y.,
Wang, Z.,
Jin, H.,
Wassell, I.,
Multi-task Adversarial Network for Disentangled Feature Learning,
CVPR18(3743-3751)
IEEE DOI
1812
Training, Generators, Task analysis,
Feature extraction, Image generation, Optimization
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
Li, R.,
Cao, W.,
Qian, S.,
Wong, H.,
Wu, S.,
Cross-domain Semantic Feature Learning via Adversarial Adaptation
Networks,
ICPR18(37-42)
IEEE DOI
1812
Feature extraction, Semantics, Task analysis, Adaptation models,
Data mining, Computational modeling, Generators,
adversarial learning
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
Dong, Y.,
Liao, F.,
Pang, T.,
Su, H.,
Zhu, J.,
Hu, X.,
Li, J.,
Boosting Adversarial Attacks with Momentum,
CVPR18(9185-9193)
IEEE DOI
1812
Iterative methods, Robustness, Training, Data models,
Adaptation models, Security
BibRef
Eykholt, K.,
Evtimov, I.,
Fernandes, E.,
Li, B.,
Rahmati, A.,
Xiao, C.,
Prakash, A.,
Kohno, T.,
Song, D.,
Robust Physical-World Attacks on Deep Learning Visual Classification,
CVPR18(1625-1634)
IEEE DOI
1812
Perturbation methods, Roads, Cameras, Visualization, Pipelines,
Autonomous vehicles, Detectors
BibRef
Hong, W.X.[Wei-Xiang],
Wang, Z.Z.[Zhen-Zhen],
Yang, M.[Ming],
Yuan, J.S.[Jun-Song],
Conditional Generative Adversarial Network for Structured Domain
Adaptation,
CVPR18(1335-1344)
IEEE DOI
1812
Semantics, Image segmentation, Generators, Training,
Adaptation models, Neural networks, Gallium nitride
BibRef
Chen, Q.C.[Qing-Chao],
Liu, Y.[Yang],
Wang, Z.W.[Zhao-Wen],
Wassell, I.[Ian],
Chetty, K.[Kevin],
Re-weighted Adversarial Adaptation Network for Unsupervised Domain
Adaptation,
CVPR18(7976-7985)
IEEE DOI
1812
Feature extraction, Training, Task analysis, Adaptation models,
Computer vision, Neural networks, Loss measurement
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
Sankaranarayanan, S.,
Balaji, Y.,
Castillo, C.D.,
Chellappa, R.,
Generate to Adapt: Aligning Domains Using Generative Adversarial
Networks,
CVPR18(8503-8512)
IEEE DOI
1812
Generators, Training, Adaptation models,
Image generation, Data models, Task analysis
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
Fang, Y.,
Yuan, Q.,
Zhang, W.,
Zhang, Z.,
Diversified Dual Domain-Adversarial Neural Networks,
ICPR18(615-620)
IEEE DOI
1812
Feature extraction, Adaptation models, Training,
Task analysis, Neural networks, Data models
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
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
Wang, J.[Jue],
Cherian, A.[Anoop],
Learning Discriminative Video Representations Using Adversarial
Perturbations,
ECCV18(II: 716-733).
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
Kang, G.L.[Guo-Liang],
Zheng, L.[Liang],
Yan, Y.[Yan],
Yang, Y.[Yi],
Deep Adversarial Attention Alignment for Unsupervised Domain
Adaptation: The Benefit of Target Expectation Maximization,
ECCV18(XI: 420-436).
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
Li, Y.[Ya],
Tian, X.[Xinmei],
Gong, M.M.[Ming-Ming],
Liu, Y.J.[Ya-Jing],
Liu, T.L.[Tong-Liang],
Zhang, K.[Kun],
Tao, D.C.[Da-Cheng],
Deep Domain Generalization via Conditional Invariant Adversarial
Networks,
ECCV18(XV: 647-663).
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, Gallium nitride, 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, Gallium nitride,
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,
Gallium nitride, Generators, Neural networks, Training
BibRef
Mukuta, Y.,
Ushiku, Y.,
Harada, T.,
Spatial-Temporal Weighted Pyramid Using Spatial Orthogonal Pooling,
CEFR-LCV17(1041-1049)
IEEE DOI
1802
Encoding, Feature extraction, Robustness,
Spatial resolution, Standards
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
Moosavi-Dezfooli, S.M.[Seyed-Mohsen],
Fawzi, A.[Alhussein],
Fawzi, O.[Omar],
Frossard, P.[Pascal],
Universal Adversarial Perturbations,
CVPR17(86-94)
IEEE DOI
1711
Computer architecture, Correlation, Neural networks, Optimization,
Robustness, Training, Visualization
BibRef
Narodytska, N.,
Kasiviswanathan, S.,
Simple Black-Box Adversarial Attacks on Deep Neural Networks,
PRIV17(1310-1318)
IEEE DOI
1709
Computer vision, Knowledge engineering, Network architecture,
Neural networks, Robustness, Training
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
Bousmalis, K.,
Silberman, N.,
Dohan, D.,
Erhan, D.,
Krishnan, D.,
Unsupervised Pixel-Level Domain Adaptation with Generative
Adversarial Networks,
CVPR17(95-104)
IEEE DOI
1711
Adaptation models, Feature extraction, Gallium nitride, Generators,
Google, Training
BibRef
Chapter on Pattern Recognition, Clustering, Statistics, Grammars, Learning, Neural Nets, Genetic Algorithms continues in
Adversarial Networks for Image Synthesis .