14.5.7.7 Adversarial Networks, Adversarial Inputs

Chapter Contents (Back)
Adversarial Networks. GAN. Deliberate noise to fool the network.

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

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


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

Lettry, L., Vanhoey, K., Van Gool, L.J.,
DARN: A Deep Adversarial Residual Network for Intrinsic Image Decomposition,
WACV18(1359-1367)
IEEE DOI 1806
feedforward neural nets, image colour analysis, learning (artificial intelligence), MPI Sintel dataset, Training BibRef

Fernando, T., Denman, S., Sridharan, S., Fookes, C.,
Task Specific Visual Saliency Prediction with Memory Augmented Conditional Generative Adversarial Networks,
WACV18(1539-1548)
IEEE DOI 1806
computer vision, feature extraction, image classification, image representation, learning (artificial intelligence), Visualization 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

Mao, X., Li, Q., Xie, H., Lau, R.Y.K., Wang, Z., Smolley, S.P.,
Least Squares Generative Adversarial Networks,
ICCV17(2813-2821)
IEEE DOI 1802
image classification, least squares approximations, unsupervised learning, LSGANs, Stability analysis 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 .


Last update:Jul 19, 2018 at 13:26:08