Rob-GAN: Generator, Discriminator, and Adversarial Attacker
暂无分享,去创建一个
[1] Yuichi Yoshida,et al. Spectral Normalization for Generative Adversarial Networks , 2018, ICLR.
[2] Nicholas Carlini,et al. On the Robustness of the CVPR 2018 White-Box Adversarial Example Defenses , 2018, ArXiv.
[3] David A. Wagner,et al. Obfuscated Gradients Give a False Sense of Security: Circumventing Defenses to Adversarial Examples , 2018, ICML.
[4] Léon Bottou,et al. Wasserstein GAN , 2017, ArXiv.
[5] P. Cochat,et al. Et al , 2008, Archives de pediatrie : organe officiel de la Societe francaise de pediatrie.
[6] Aleksander Madry,et al. Towards Deep Learning Models Resistant to Adversarial Attacks , 2017, ICLR.
[7] Jonathon Shlens,et al. Explaining and Harnessing Adversarial Examples , 2014, ICLR.
[8] Tatsuya Harada,et al. Learning from Between-class Examples for Deep Sound Recognition , 2017, ICLR.
[9] Joan Bruna,et al. Intriguing properties of neural networks , 2013, ICLR.
[10] Léon Bottou,et al. Towards Principled Methods for Training Generative Adversarial Networks , 2017, ICLR.
[11] Hongyi Zhang,et al. mixup: Beyond Empirical Risk Minimization , 2017, ICLR.
[12] Mingyan Liu,et al. Generating Adversarial Examples with Adversarial Networks , 2018, IJCAI.
[13] Rob Fergus,et al. Deep Generative Image Models using a Laplacian Pyramid of Adversarial Networks , 2015, NIPS.
[14] Alex Graves,et al. Conditional Image Generation with PixelCNN Decoders , 2016, NIPS.
[15] Kamyar Azizzadenesheli,et al. Stochastic Activation Pruning for Robust Adversarial Defense , 2018, ICLR.
[16] James Bailey,et al. Characterizing Adversarial Subspaces Using Local Intrinsic Dimensionality , 2018, ICLR.
[17] Yoshua Bengio,et al. Generative Adversarial Nets , 2014, NIPS.
[18] Moustapha Cissé,et al. Parseval Networks: Improving Robustness to Adversarial Examples , 2017, ICML.
[19] David Berthelot,et al. BEGAN: Boundary Equilibrium Generative Adversarial Networks , 2017, ArXiv.
[20] Alexei A. Efros,et al. Image-to-Image Translation with Conditional Adversarial Networks , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[21] Peter Norvig,et al. The Unreasonable Effectiveness of Data , 2009, IEEE Intelligent Systems.
[22] Rama Chellappa,et al. Defense-GAN: Protecting Classifiers Against Adversarial Attacks Using Generative Models , 2018, ICLR.
[23] Jonathon Shlens,et al. Conditional Image Synthesis with Auxiliary Classifier GANs , 2016, ICML.
[24] Takeru Miyato,et al. cGANs with Projection Discriminator , 2018, ICLR.
[25] Sepp Hochreiter,et al. Coulomb GANs: Provably Optimal Nash Equilibria via Potential Fields , 2017, ICLR.
[26] Alan L. Yuille,et al. Mitigating adversarial effects through randomization , 2017, ICLR.
[27] Soumith Chintala,et al. Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks , 2015, ICLR.
[28] John C. Duchi,et al. Certifiable Distributional Robustness with Principled Adversarial Training , 2017, ArXiv.
[29] Geoffrey E. Hinton,et al. ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.
[30] Aaron C. Courville,et al. Improved Training of Wasserstein GANs , 2017, NIPS.
[31] Hiroshi Inoue,et al. Data Augmentation by Pairing Samples for Images Classification , 2018, ArXiv.
[32] Richard S. Zemel,et al. Generative Moment Matching Networks , 2015, ICML.
[33] Ananthram Swami,et al. Distillation as a Defense to Adversarial Perturbations Against Deep Neural Networks , 2015, 2016 IEEE Symposium on Security and Privacy (SP).
[34] Simon Osindero,et al. Conditional Generative Adversarial Nets , 2014, ArXiv.
[35] John E. Hopcroft,et al. Stacked Generative Adversarial Networks , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[36] Jaakko Lehtinen,et al. Progressive Growing of GANs for Improved Quality, Stability, and Variation , 2017, ICLR.
[37] Colin Raffel,et al. Thermometer Encoding: One Hot Way To Resist Adversarial Examples , 2018, ICLR.
[38] Max Welling,et al. Auto-Encoding Variational Bayes , 2013, ICLR.
[39] Yann LeCun,et al. Deep multi-scale video prediction beyond mean square error , 2015, ICLR.
[40] Yang Song,et al. PixelDefend: Leveraging Generative Models to Understand and Defend against Adversarial Examples , 2017, ICLR.
[41] David A. Wagner,et al. Towards Evaluating the Robustness of Neural Networks , 2016, 2017 IEEE Symposium on Security and Privacy (SP).
[42] John C. Duchi,et al. Certifying Some Distributional Robustness with Principled Adversarial Training , 2017, ICLR.