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.[Yiwen],
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.[Wenkai],
Yang, G.D.[Guo-Dong],
Yu, J.[Junzhi],
Liang, Z.[Zize],
Real-time segmentation of various insulators using generative
adversarial networks,
IET-CV(12), No. 5, August 2018, pp. 596-602.
DOI Link
1807
BibRef
Huang, B.[Bin],
Chen, W.H.[Wei-Hai],
Wu, X.[Xingming],
Lin, C.L.[Chun-Liang],
Suganthan, P.N.[Ponnuthurai Nagaratnam],
High-quality face image generated with conditional boundary
equilibrium generative adversarial networks,
PRL(111), 2018, pp. 72-79.
Elsevier DOI
1808
Super resolution, Generative Adversarial Network (GAN),
Face hallucination, Convolutional Neural Network (CNN)
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
Tan, W.R.,
Chan, C.S.,
Aguirre, H.E.,
Tanaka, K.,
Improved ArtGAN for Conditional Synthesis of Natural Image and
Artwork,
IP(28), No. 1, January 2019, pp. 394-409.
IEEE DOI
1810
Generators, Image generation, Image quality,
Generative adversarial networks, Training, Image resolution,
ArtGAN
BibRef
Chen, X.,
Xu, C.,
Yang, X.,
Song, L.,
Tao, D.,
Gated-GAN: Adversarial Gated Networks for Multi-Collection Style
Transfer,
IP(28), No. 2, February 2019, pp. 546-560.
IEEE DOI
1811
codecs, image classification, image coding, image reconstruction,
rendering (computer graphics), Gated-GAN,
adversarial generative networks
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
Zhang, L.,
Gonzalez-Garcia, A.,
van de Weijer, J.[Joost],
Danelljan, M.[Martin],
Khan, F.S.[Fahad Shahbaz],
Synthetic Data Generation for End-to-End Thermal Infrared Tracking,
IP(28), No. 4, April 2019, pp. 1837-1850.
IEEE DOI
1901
convolution, feature extraction, feedforward neural nets,
image colour analysis, image motion analysis, image sequences,
generative networks
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
Wu, K.W.[Ke-Wei],
Gao, Y.[Yang],
Ma, H.L.[Hai-Long],
Sun, Y.X.[Yong-Xuan],
Yao, T.T.[Ting-Ting],
Xie, Z.[Zhao],
A deep generative directed network for scene depth ordering,
JVCIR(58), 2019, pp. 554-564.
Elsevier DOI
1901
Deep generative directed-network, Depth ordering,
Hidden Markov field, DenseNet
See also Densely Connected Convolutional Networks.
BibRef
Aggarwal, H.K.[Hemant K.],
Mani, M.P.[Merry P.],
Jacob, M.[Mathews],
MoDL: Model-Based Deep Learning Architecture for Inverse Problems,
MedImg(38), No. 2, February 2019, pp. 394-405.
IEEE DOI
1902
Image reconstruction, Training data, Training, Optimization, Imaging,
Numerical models, Machine learning, Deep learning,
convolutional neural network
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
Li, Y.[Yi],
Song, L.X.[Ling-Xiao],
Wu, X.[Xiang],
He, R.[Ran],
Tan, T.N.[Tie-Niu],
Learning a bi-level adversarial network with global and local
perception for makeup-invariant face verification,
PR(90), 2019, pp. 99-108.
Elsevier DOI
1903
Face verification, Makeup-invariant, Generative adversarial network
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
Tran, L.[Linh],
Kossaifi, J.[Jean],
Panagakis, Y.[Yannis],
Pantic, M.[Maja],
Disentangling Geometry and Appearance with Regularised Geometry-Aware
Generative Adversarial Networks,
IJCV(127), No. 6-7, June 2019, pp. 824-844.
Springer DOI
1906
BibRef
Earlier: A2, A1, A3, A4:
GAGAN: Geometry-Aware Generative Adversarial Networks,
CVPR18(878-887)
IEEE DOI
1812
Shape, Geometry, Generators, Training, Computational modeling, Face
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
Yu, B.,
Zhou, L.,
Wang, L.,
Shi, Y.,
Fripp, J.,
Bourgeat, P.,
Ea-GANs: Edge-Aware Generative Adversarial Networks for
Cross-Modality MR Image Synthesis,
MedImg(38), No. 7, July 2019, pp. 1750-1762.
IEEE DOI
1907
Image edge detection, Image generation,
Generative adversarial networks, Generators, Imaging,
brain
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
Xu, W.J.[Wen-Ju],
Keshmiri, S.[Shawn],
Wang, G.H.[Guang-Hui],
Adversarially Approximated Autoencoder for Image Generation and
Manipulation,
MultMed(21), No. 9, September 2019, pp. 2387-2396.
IEEE DOI
1909
Training, Neural networks, Data models,
Image reconstruction, Image generation,
latent manifold structure
BibRef
Regmi, K.[Krishna],
Borji, A.[Ali],
Cross-view image synthesis using geometry-guided conditional GANs,
CVIU(187), 2019, pp. 102788.
Elsevier DOI
1909
BibRef
Earlier:
Cross-View Image Synthesis Using Conditional GANs,
CVPR18(3501-3510)
IEEE DOI
1812
Synthesis, GANs, Cross-view, View-translation, Homography.
Image segmentation, Task analysis, Generators,
Generative adversarial networks, Image generation, Semantics
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
Li, Y.,
Tang, S.,
Zhang, R.,
Zhang, Y.,
Li, J.,
Yan, S.,
Asymmetric GAN for Unpaired Image-to-Image Translation,
IP(28), No. 12, December 2019, pp. 5881-5896.
IEEE DOI
1909
Sensitivity, Training, Generators,
Generative adversarial networks, Complexity theory,
unpaired translation
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
Gammulle, H.[Harshala],
Denman, S.[Simon],
Sridharan, S.[Sridha],
Fookes, C.[Clinton],
Fine-grained action segmentation using the semi-supervised action GAN,
PR(98), 2020, pp. 107039.
Elsevier DOI
1911
Human action segmentation, Generative adversarial networks, Context modelling
BibRef
Gammulle, H.[Harshala],
Fernando, T.[Tharindu],
Denman, S.[Simon],
Sridharan, S.[Sridha],
Fookes, C.[Clinton],
Coupled Generative Adversarial Network for Continuous Fine-Grained
Action Segmentation,
WACV19(200-209)
IEEE DOI
1904
BibRef
Earlier: A2, A3, A4, A5, Only:
Task Specific Visual Saliency Prediction with Memory Augmented
Conditional Generative Adversarial Networks,
WACV18(1539-1548)
IEEE DOI
1806
feature extraction, image recognition, image representation,
image segmentation, image sequences,
Couplings.
image classification,
image representation, learning (artificial intelligence),
Visualization.
BibRef
Zhang, Y.B.[Yong-Bing],
Liu, S.Y.[Si-Yuan],
Dong, C.[Chao],
Zhang, X.F.[Xin-Feng],
Yuan, Y.[Yuan],
Multiple Cycle-in-Cycle Generative Adversarial Networks for
Unsupervised Image Super-Resolution,
IP(29), No. , 2020, pp. 1101-1112.
IEEE DOI
1911
Training, Kernel, Degradation, Interpolation, Deep learning,
Super resolution, unsupervised learning, generative adversarial networks
BibRef
Yuan, Y.,
Liu, S.,
Zhang, J.,
Zhang, Y.,
Dong, C.,
Lin, L.,
Unsupervised Image Super-Resolution Using Cycle-in-Cycle Generative
Adversarial Networks,
Restoration18(814-81409)
IEEE DOI
1812
Image resolution, Kernel, Training, Generators,
Degradation, Unsupervised learning
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
Guo, Y.,
Chen, Q.,
Chen, J.,
Wu, Q.,
Shi, Q.,
Tan, M.,
Auto-Embedding Generative Adversarial Networks For High Resolution
Image Synthesis,
MultMed(21), No. 11, November 2019, pp. 2726-2737.
IEEE DOI
1911
Image resolution, Generative adversarial networks,
Training, Data mining, Feature extraction, autoencoder
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
Liu, S.Y.[Song-Yan],
Zhao, C.Y.[Chao-Yang],
Gao, Y.Z.[Yun-Ze],
Wang, J.Q.[Jin-Qiao],
Tang, M.[Ming],
Adversarial image generation by combining content and style,
IET-IPR(13), No. 14, 12 December 2019, pp. 2716-2723.
DOI Link
1912
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
Miller, D.J.,
Xiang, Z.,
Kesidis, G.,
Adversarial Learning Targeting Deep Neural Network Classification:
A Comprehensive Review of Defenses Against Attacks,
PIEEE(108), No. 3, March 2020, pp. 402-433.
IEEE DOI
2003
Training data, Neural networks, Reverse engineering,
Machine learning, Robustness, Training data, Feature extraction,
white box
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
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
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
Moosavi-Dezfooli, S.M.[Seyed-Mohsen],
Fawzi, A.[Alhussein],
Uesato, J.[Jonathan],
Frossard, P.[Pascal],
Robustness via Curvature Regularization, and Vice Versa,
CVPR19(9070-9078).
IEEE DOI
2002
Adversarial training leads to more linear boundaries.
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
Zhan, F.N.[Fang-Neng],
Zhu, H.Y.[Hong-Yuan],
Lu, S.J.[Shi-Jian],
Spatial Fusion GAN for Image Synthesis,
CVPR19(3648-3657).
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.J.[Junbo 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
Abbasnejad, M.E.[M. Ehsan],
Shi, Q.[Qinfeng],
van den Hengel, A.[Anton],
Liu, L.Q.[Ling-Qiao],
A Generative Adversarial Density Estimator,
CVPR19(10774-10783).
IEEE DOI
2002
BibRef
Taran, O.[Olga],
Rezaeifar, S.[Shideh],
Holotyak, T.[Taras],
Voloshynovskiy, S.[Slava],
Defending Against Adversarial Attacks by Randomized Diversification,
CVPR19(11218-11225).
IEEE DOI
2002
BibRef
Jaiswal, A.[Ayush],
Wu, Y.[Yue],
AbdAlmageed, 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,
CVPR19(11332-11341).
IEEE DOI
2002
BibRef
Yao, Z.[Zhewei],
Gholami, A.[Amir],
Xu, P.[Peng],
Keutzer, K.[Kurt],
Mahoney, M.W.[Michael W.],
Trust Region Based Adversarial Attack on Neural Networks,
CVPR19(11342-11351).
IEEE DOI
2002
BibRef
Liang, J.[Jian],
Cao, Y.[Yuren],
Zhang, C.[Chenbin],
Chang, S.[Shiyu],
Bai, K.[Kun],
Xu, Z.[Zenglin],
Additive Adversarial Learning for Unbiased Authentication,
CVPR19(11420-11429).
IEEE DOI
2002
BibRef
Sun, B.[Bo],
Tsai, N.H.[Nian-Hsuan],
Liu, F.C.[Fang-Chen],
Yu, R.[Ronald],
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2002
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2002
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2002
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SketchGAN: Joint Sketch Completion and Recognition With Generative
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IEEE DOI
2002
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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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IEEE DOI
2002
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How to Make a Pizza: Learning a Compositional Layer-Based GAN Model,
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2002
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2002
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Enhancing TripleGAN for Semi-Supervised Conditional Instance Synthesis
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2002
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F-VAEGAN-D2: A Feature Generating Framework for Any-Shot Learning,
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IEEE DOI
2002
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Zhao, Z.Z.[Zhi-Zhen],
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IEEE DOI
2002
BibRef
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Synthetic Thermal Background and Object Texture Generation Using
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PTVSBB19(149-154).
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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
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Gupta, P.[Puneet],
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MLAttack: Fooling Semantic Segmentation Networks by Multi-layer Attacks,
GCPR19(401-413).
Springer DOI
1911
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Chang, J.H.[Jian-Hui],
Mao, Q.[Qi],
Zhao, Z.H.[Zheng-Hui],
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Wang, S.Q.[Shi-Qi],
Zhu, H.[Hong],
Ma, S.W.[Si-Wei],
Layered Conceptual Image Compression Via Deep Semantic Synthesis,
ICIP19(694-698)
IEEE DOI
1910
Integrating the advantages of both variational auto-encoders (VAEs)
and generative adversarial networks (GANs).
Image compression, generative models, low bitrate coding
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Kim, B.,
Lee, J.,
Kim, K.,
Kim, S.,
Kim, J.,
Collaborative Method for Incremental Learning on Classification and
Generation,
ICIP19(390-394)
IEEE DOI
1910
Incremental Learning, Deep Neural Networks, Generative Adversarial Networks
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Yao, H.,
Regan, M.,
Yang, Y.,
Ren, Y.,
Image Decomposition and Classification Through a Generative Model,
ICIP19(400-404)
IEEE DOI
1910
Generative model, classification, adversarial defense
BibRef
Yin, J.,
Li, W.,
Han, B.,
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
Miyazato, S.,
Wang, X.,
Yamasaki, T.,
Aizawa, K.,
Reinforcing the Robustness of a Deep Neural Network to Adversarial
Examples by Using Color Quantization of Training Image Data,
ICIP19(884-888)
IEEE DOI
1910
convolutional neural network, adversarial example, color quantization
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
Ramanathan, T.,
Manimaran, A.,
You, S.,
Kuo, C.J.,
Robustness of Saak Transform Against Adversarial Attacks,
ICIP19(2531-2535)
IEEE DOI
1910
Saak transform, Adversarial attacks, Deep Neural Networks, Image Classification
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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
Yang, C.H.,
Liu, Y.,
Chen, P.,
Ma, X.,
Tsai, Y.J.,
When Causal Intervention Meets Adversarial Examples and Image Masking
for Deep Neural Networks,
ICIP19(3811-3815)
IEEE DOI
1910
Causal Reasoning, Adversarial Example, Adversarial Robustness,
Interpretable Deep Learning, Visual Reasoning
BibRef
Ji, J.,
Zhong, B.,
Ma, K.,
Multi-Scale Defense of Adversarial Images,
ICIP19(4070-4074)
IEEE DOI
1910
deep learning, adversarial images, defense, multi-scale, image evolution
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
Park, M.,
Kim, H.G.,
Ro, Y.M.[Y. Man],
Generative Guiding Block: Synthesizing Realistic Looking Variants
Capable of Even Large Change Demands,
ICIP19(4444-4448)
IEEE DOI
1910
Deep learning, adversarial learning, variation image synthesis,
feature enhancement
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
Agarwal, C.,
Nguyen, A.,
Schonfeld, D.,
Improving Robustness to Adversarial Examples by Encouraging
Discriminative Features,
ICIP19(3801-3805)
IEEE DOI
1910
Adversarial Machine Learning, Robustness, Defenses, Deep Learning
BibRef
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
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
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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
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
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Chen, K.[Kevin],
Choy, C.B.[Christopher B.],
Savva, M.[Manolis],
Chang, A.X.[Angel X.],
Funkhouser, T.[Thomas],
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Text2Shape: Generating Shapes from Natural Language by Learning Joint
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Springer DOI
1906
Generating colored 3D shapes from natural language.
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Vo, D.M.[Duc Minh],
Sugimoto, A.[Akihiro],
Paired-D GAN for Semantic Image Synthesis,
ACCV18(IV:468-484).
Springer DOI
1906
BibRef
Ge, H.W.[Hong-Wei],
Yao, Y.[Yao],
Chen, Z.[Zheng],
Sun, L.[Liang],
Unsupervised Transformation Network Based on GANs for Target-Domain
Oriented Multi-Domain Image Translation,
ACCV18(II:398-413).
Springer DOI
1906
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
Hsu, S.Y.[Shu-Yu],
Yang, C.Y.[Chih-Yuan],
Huang, C.C.[Chi-Chia],
Hsu, J.Y.J.[Jane Yung-Jen],
SemiStarGAN: Semi-supervised Generative Adversarial Networks for
Multi-domain Image-to-Image Translation,
ACCV18(IV:338-353).
Springer DOI
1906
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Heljakka, A.[Ari],
Solin, A.[Arno],
Kannala, J.H.[Ju-Ho],
Pioneer Networks: Progressively Growing Generative Autoencoder,
ACCV18(I:22-38).
Springer DOI
1906
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Alberti, M.[Michele],
Pondenkandath, V.[Vinaychandran],
Würsch, M.[Marcel],
Bouillon, M.[Manuel],
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Ingold, R.[Rolf],
Liwicki, M.[Marcus],
Are You Tampering with My Data?,
Objectionable18(II:296-312).
Springer DOI
1905
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Belagiannis, V.[Vasileios],
Farshad, A.[Azade],
Galasso, F.[Fabio],
Adversarial Network Compression,
CEFR-LCV18(IV:431-449).
Springer DOI
1905
BibRef
Öngün, C.[Cihan],
Temizel, A.[Alptekin],
Paired 3D Model Generation with Conditional Generative Adversarial
Networks,
3D-Wild18(I:473-487).
Springer DOI
1905
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Croce, F.[Francesco],
Hein, M.[Matthias],
A Randomized Gradient-Free Attack on ReLU Networks,
GCPR18(215-227).
Springer DOI
1905
BibRef
Taran, O.[Olga],
Rezaeifar, S.[Shideh],
Voloshynovskiy, S.[Slava],
Bridging Machine Learning and Cryptography in Defence Against
Adversarial Attacks,
Objectionable18(II:267-279).
Springer DOI
1905
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Carrara, F.[Fabio],
Becarelli, R.[Rudy],
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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
Networks,
Egocentric18(V:406-422).
Springer DOI
1905
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
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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
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
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IWDW18(83-92).
Springer DOI
1905
BibRef
Naseer, M.,
Khan, S.,
Porikli, F.,
Local Gradients Smoothing: Defense Against Localized Adversarial
Attacks,
WACV19(1300-1307)
IEEE DOI
1904
data compression, feature extraction, gradient methods,
image classification, image coding, image representation,
High frequency
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
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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
Prakash, A.,
Moran, N.,
Garber, S.,
DiLillo, A.,
Storer, J.,
Deflecting Adversarial Attacks with Pixel Deflection,
CVPR18(8571-8580)
IEEE DOI
1812
Perturbation methods, Transforms, Minimization, Robustness,
Noise reduction, Training, Computer vision
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
Akhtar, N.,
Liu, J.,
Mian, A.,
Defense Against Universal Adversarial Perturbations,
CVPR18(3389-3398)
IEEE DOI
1812
Perturbation methods, Training, Computational modeling, Detectors,
Neural networks, Robustness, Integrated circuits
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
Zhang, Z.,
Xie, Y.,
Yang, L.,
Photographic Text-to-Image Synthesis with a Hierarchically-Nested
Adversarial Network,
CVPR18(6199-6208)
IEEE DOI
1812
Generators, Training, Image resolution,
Task analysis, Semantics, Measurement
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
Behpour, S.,
Xing, W.,
Ziebart, B.D.,
ARC: Adversarial Robust Cuts for Semi-Supervised and Multi-label
Classification,
WiCV18(1986-19862)
IEEE DOI
1812
Markov random fields, Task analysis, Training, Testing,
Support vector machines, Fasteners, Games
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
Liao, F.,
Liang, M.,
Dong, Y.,
Pang, T.,
Hu, X.,
Zhu, J.,
Defense Against Adversarial Attacks Using High-Level Representation
Guided Denoiser,
CVPR18(1778-1787)
IEEE DOI
1812
Training, Perturbation methods, Noise reduction,
Image reconstruction, Predictive models, Neural networks, Adaptation models
BibRef
Dolhansky, B.,
Ferrer, C.C.,
Eye In-painting with Exemplar Generative Adversarial Networks,
CVPR18(7902-7911)
IEEE DOI
1812
Face, Generators, Training,
Generative adversarial networks, Task analysis, Painting
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
Gong, Y.,
Karanam, S.,
Wu, Z.,
Peng, K.,
Ernst, J.,
Doerschuk, P.C.,
Learning Compositional Visual Concepts with Mutual Consistency,
CVPR18(8659-8668)
IEEE DOI
1812
Training data, Training, Image generation,
Generative adversarial networks, Face, Semantics.
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
Chen, W.,
Hays, J.,
SketchyGAN: Towards Diverse and Realistic Sketch to Image Synthesis,
CVPR18(9416-9425)
IEEE DOI
1812
Image edge detection, Image generation, Training,
Databases, Task analysis, Generative adversarial networks
BibRef
Lin, C.,
Yumer, E.,
Wang, O.,
Shechtman, E.,
Lucey, S.,
ST-GAN: Spatial Transformer Generative Adversarial Networks for Image
Compositing,
CVPR18(9455-9464)
IEEE DOI
1812
Training, Generators, Image generation, Manifolds,
Generative adversarial networks, Games
BibRef
Chen, Y.,
Lai, Y.,
Liu, Y.,
CartoonGAN: Generative Adversarial Networks for Photo Cartoonization,
CVPR18(9465-9474)
IEEE DOI
1812
Training, Generative adversarial networks,
Manifolds, Image edge detection, Automobiles, Training data
BibRef
Barsoum, E.,
Kender, J.,
Liu, Z.,
HP-GAN: Probabilistic 3D Human Motion Prediction via GAN,
Joint18(1499-149909)
IEEE DOI
1812
Predictive models,
Generative adversarial networks, Probabilistic logic, Generators,
Prediction algorithms
BibRef
Liu, Y.,
Guo, Y.,
Chen, W.,
Lew, M.S.,
An Extensive Study of Cycle-Consistent Generative Networks for
Image-to-Image Translation,
ICPR18(219-224)
IEEE DOI
1812
Generators, Task analysis, Image reconstruction,
Generative adversarial networks, Stacking, Bridges
BibRef
Li, D.,
Zhang, M.,
Chen, W.,
Feng, G.,
Facial Attribute Editing by Latent Space Adversarial Variational
Autoencoders,
ICPR18(1337-1342)
IEEE DOI
1812
Training, Decoding, Image reconstruction, Face,
Facial features, Generative adversarial networks
BibRef
Ma, R.,
Hu, H.,
Perceptual Face Completion using a Local-Global Generative
Adversarial Network,
ICPR18(1670-1675)
IEEE DOI
1812
Face, Image reconstruction, Training, Feature extraction, Semantics,
Task analysis, Generative adversarial networks, completion,
perceptual network
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
Hu, L.,
Kan, M.,
Shan, S.,
Chen, X.,
Duplex Generative Adversarial Network for Unsupervised Domain
Adaptation,
CVPR18(1498-1507)
IEEE DOI
1812
Generators, Generative adversarial networks,
Measurement, Feature extraction, Testing, Object recognition
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
Tulyakov, S.,
Liu, M.,
Yang, X.,
Kautz, J.,
MoCoGAN: Decomposing Motion and Content for Video Generation,
CVPR18(1526-1535)
IEEE DOI
1812
Generators, Training, Image generation, Trajectory,
Generative adversarial networks, Visualization
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
Xiong, W.,
Luo, W.,
Ma, L.,
Liu, W.,
Luo, J.,
Learning to Generate Time-Lapse Videos Using Multi-stage Dynamic
Generative Adversarial Networks,
CVPR18(2364-2373)
IEEE DOI
1812
Videos, Generative adversarial networks, Generators,
Dynamics, Convolution
BibRef
Chen, Y.,
Lin, H.,
Shu, M.,
Li, R.,
Tao, X.,
Shen, X.,
Ye, Y.,
Jia, J.,
Facelet-Bank for Fast Portrait Manipulation,
CVPR18(3541-3549)
IEEE DOI
1812
Face, Training, Interpolation, Decoding,
Generative adversarial networks, Semantics, Transforms
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
Volpi, R.,
Morerio, P.,
Savarese, S.,
Murino, V.,
Adversarial Feature Augmentation for Unsupervised Domain Adaptation,
CVPR18(5495-5504)
IEEE DOI
1812
Feature extraction, Training, Games,
Generative adversarial networks, Generators, Neural networks
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
Wang, T.,
Liu, M.,
Zhu, J.,
Tao, A.,
Kautz, J.,
Catanzaro, B.,
High-Resolution Image Synthesis and Semantic Manipulation with
Conditional GANs,
CVPR18(8798-8807)
IEEE DOI
1812
Generators, Image resolution, Semantics, Training,
Image generation, Task analysis
BibRef
Ghosh, A.,
Kulharia, V.,
Namboodiri, V.,
Torr, P.H.S.,
Dokania, P.K.,
Multi-agent Diverse Generative Adversarial Networks,
CVPR18(8513-8521)
IEEE DOI
1812
Generators, Task analysis, Training,
Generative adversarial networks, Computer architecture, Face
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
Ouyang, X.,
Zhang, X.,
Ma, D.,
Agam, G.,
Generating Image Sequence from Description with LSTM Conditional GAN,
ICPR18(2456-2461)
IEEE DOI
1812
Generators, Semantics, Image generation, Training,
Logic gates, Neural 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
Liu, X.,
Meng, G.,
Xiang, S.,
Pan, C.,
Semantic Image Synthesis via Conditional Cycle-Generative Adversarial
Networks,
ICPR18(988-993)
IEEE DOI
1812
Birds, Generators, Generative adversarial networks, Semantics,
Training, 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
Karim, R.,
Islam, M.A.,
Mohammed, N.,
Bruce, N.D.B.,
On the Robustness of Deep Learning Models to Universal Adversarial
Attack,
CRV18(55-62)
IEEE DOI
1812
Perturbation methods, Computational modeling, Neural networks,
Task analysis, Image segmentation, Data models, Semantics,
Semantic Segmentation
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
Chen, X.Y.[Xin-Yuan],
Xu, C.[Chang],
Yang, X.K.[Xiao-Kang],
Tao, D.C.[Da-Cheng],
Attention-GAN for Object Transfiguration in Wild Images,
ECCV18(II: 167-184).
Springer DOI
1810
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
He, Y.[Yang],
Schiele, B.[Bernt],
Fritz, M.[Mario],
Diverse Conditional Image Generation by Stochastic Regression with
Latent Drop-Out Codes,
ECCV18(XVI: 422-437).
Springer DOI
1810
BibRef
Wang, Y.X.[Ya-Xing],
Wu, C.S.[Chen-Shen],
Herranz, L.[Luis],
van de Weijer, J.[Joost],
Gonzalez-Garcia, A.[Abel],
Raducanu, B.[Bogdan],
Transferring GANs: Generating Images from Limited Data,
ECCV18(VI: 220-236).
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
Bodla, N.[Navaneeth],
Hua, G.[Gang],
Chellappa, R.[Rama],
Semi-supervised FusedGAN for Conditional Image Generation,
ECCV18(VI: 689-704).
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
Jakubovitz, D.[Daniel],
Giryes, R.[Raja],
Improving DNN Robustness to Adversarial Attacks Using Jacobian
Regularization,
ECCV18(XII: 525-541).
Springer DOI
1810
BibRef
Gecer, B.[Baris],
Bhattarai, B.[Binod],
Kittler, J.V.[Josef V.],
Kim, T.K.[Tae-Kyun],
Semi-supervised Adversarial Learning to Generate Photorealistic Face
Images of New Identities from 3D Morphable Model,
ECCV18(XI: 230-248).
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
Felsen, P.[Panna],
Lucey, P.[Patrick],
Ganguly, S.[Sujoy],
Where Will They Go? Predicting Fine-Grained Adversarial Multi-agent
Motion Using Conditional Variational Autoencoders,
ECCV18(XI: 761-776).
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, M.J.[Min-Jun],
Huang, H.Z.[Hao-Zhi],
Ma, L.[Lin],
Liu, W.[Wei],
Zhang, T.[Tong],
Jiang, Y.G.[Yu-Gang],
Unsupervised Image-to-Image Translation with Stacked Cycle-Consistent
Adversarial Networks,
ECCV18(IX: 186-201).
Springer DOI
1810
BibRef
Wang, C.[Chao],
Zheng, H.[Haiyong],
Yu, Z.B.[Zhi-Bin],
Zheng, Z.Q.[Zi-Qiang],
Gu, Z.[Zhaorui],
Zheng, B.[Bing],
Discriminative Region Proposal Adversarial Networks for High-Quality
Image-to-Image Translation,
ECCV18(I: 796-812).
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
Lu, Y.Y.[Yong-Yi],
Wu, S.Z.[Shang-Zhe],
Tai, Y.W.[Yu-Wing],
Tang, C.K.[Chi-Keung],
Image Generation from Sketch Constraint Using Contextual GAN,
ECCV18(XVI: 213-228).
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
Jin, G.,
Zhang, D.,
Dai, F.,
Guo, J.,
Ma, Y.,
Zhang, Y.,
Semantic Preserving Hash Coding Through VAE-GAN,
ICIP18(1997-2001)
IEEE DOI
1809
Semantics, Generators, Machine learning, Training, Binary codes,
Image generation, Image retrieval,
Generative adversarial network
BibRef
Li, C.,
Wang, Z.,
Qi, H.,
Fast-Converging Conditional Generative Adversarial Networks for Image
Synthesis,
ICIP18(2132-2136)
IEEE DOI
1809
Generators, Generative adversarial networks,
Convergence, Image generation, Visualization, Training,
image synthesis
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
Cheung, S.S.[S. Samson],
Wildfeuer, H.,
Nikkhah, M.,
Zhu, X.,
Tan, W.,
Learning Sensitive Images Using Generative Models,
ICIP18(4128-4132)
IEEE DOI
1809
Training, Face, Generative adversarial networks,
Privacy, Data privacy, Machine learning,
face processing
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
Zhang, Z.,
Sun, Y.,
Yu, J.,
A Cross-Layer Based Network for Faster Image Generation,
ICIP18(3903-3907)
IEEE DOI
1809
Generators, Training, Computer architecture,
Image generation, Face, Generative adversarial networks,
cross-layer connection
BibRef
Rozsa, A.,
Gunther, M.,
Boult, T.E.,
Towards Robust Deep Neural Networks with BANG,
WACV18(803-811)
IEEE DOI
1806
image processing, learning (artificial intelligence),
neural nets, BANG technique, adversarial image utilization,
Training
BibRef
Peleg, I.,
Wolf, L.B.[Lior B.],
Structured GANs,
WACV18(719-728)
IEEE DOI
1806
image processing, learning (artificial intelligence),
neural nets, face image synthesis,
Training
BibRef
Chai, W.,
Deng, W.,
Shen, H.,
Cross-Generating GAN for Facial Identity Preserving,
FG18(130-134)
IEEE DOI
1806
Encoding, Face, Face recognition, Feature extraction,
Finite impulse response filters, Lighting, CG GAN,
Multi PIE
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
Yi, Z.[Zili],
Zhang, H.[Hao],
Tan, P.[Ping],
Gong, M.L.[Ming-Lun],
DualGAN: Unsupervised Dual Learning for Image-to-Image Translation,
ICCV17(2868-2876)
IEEE DOI
1802
computational complexity, image reconstruction,
unsupervised learning.
BibRef
Palazzo, S.,
Spampinato, C.,
Kavasidis, I.,
Giordano, D.,
Shah, M.,
Generative Adversarial Networks Conditioned by Brain Signals,
ICCV17(3430-3438)
IEEE DOI
1802
brain, electroencephalography, image representation,
learning (artificial intelligence), medical image processing,
Visualization
BibRef
Tung, H.Y.F.,
Harley, A.W.,
Seto, W.,
Fragkiadaki, K.,
Adversarial Inverse Graphics Networks: Learning 2D-to-3D Lifting and
Image-to-Image Translation from Unpaired Supervision,
ICCV17(4364-4372)
IEEE DOI
1802
face recognition, image matching, image resolution,
learning (artificial intelligence), motion estimation,
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
Alvarez-Gila, A.,
van de Weijer, J.[Joost],
Garrote, E.,
Adversarial Networks for Spatial Context-Aware Spectral Image
Reconstruction from RGB,
PBVDL17(480-490)
IEEE DOI
1802
Generators, Hyperspectral imaging, Image reconstruction, Imaging, Semantics
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
Choe, J.,
Park, S.,
Kim, K.,
Park, J.H.,
Kim, D.,
Shim, H.,
Face Generation for Low-Shot Learning Using Generative Adversarial
Networks,
Million17(1940-1948)
IEEE DOI
1802
Face, Face recognition, Feature extraction, Gallium nitride,
Generators, Image reconstruction, Training
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
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
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
Lu, J.,
Issaranon, T.,
Forsyth, D.A.,
SafetyNet: Detecting and Rejecting Adversarial Examples Robustly,
ICCV17(446-454)
IEEE DOI
1802
image colour analysis, image reconstruction,
learning (artificial intelligence), neural nets,
BibRef
Chapter on Pattern Recognition, Clustering, Statistics, Grammars, Learning, Neural Nets, Genetic Algorithms continues in
Bayesian Learning, Bayes Network, Bayesian Networks .