随笔分类 - Robust Learning
摘要：Gowal S., Dvijotham K., Stanforth R., Bunel R., Qin C., Uesato J., Arandjelovic R., Mann T. & Kohli P. Scalable verified training for provably robust
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摘要：Jiang Z., Chen T., Chen T. & Wang Z. Robust Pre-Training by Adversarial Contrastive Learning. NIPS, 2020. 概 本文介绍了一种利用对比学习进行对抗预训练的方法. 主要内容 思想是很简单的, 就是利
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摘要：Croce F. & Hein M. Reliable evaluation of adversarial robustness with an ensemble of diverse parameter-free attacks. In International Conference on Ma
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摘要：Pang T., Yang X., Dong Y., Xu K., Su H., Zhu J. Boosting Adversarial Training with Hypersphere Embedding. arXiv preprint arXIv 2002.08619 概 在最后一层, 对we
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摘要：Uncovering the Limits of Adversarial Training against Norm-Bounded Adversarial Examples Gowal S., Qin C., Uesato J., Mann T. & Kohli P. Uncovering the
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摘要：Kim M., Tack J. & Hwang S. Adversarial Self-Supervised Contrastive Learning. In Advances in Neural Information Processing Systems, 2020. 概 这篇文章提出了对比学习
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摘要：Tsiligkaridis T., Roberts J. Second Order Optimization for Adversarial Robustness and Interpretability. arXiv preprint axXiv 2009.04923, 2020. 概 也算是一种
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摘要：Cohen J., Rosenfeld E., Kolter J. Certified Adversarial Robustness via Randomized Smoothing. International Conference on Machine Learning (ICML), 2019
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摘要：Lecuyer M, Atlidakis V, Geambasu R, et al. Certified Robustness to Adversarial Examples with Differential Privacy[C]. ieee symposium on security and p
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摘要：Li Y., Xie L., Zhang Y., Zhang R., Wang Y., Tian Q., Defending Adversarial Attacks by Correcting logits[J]. arXiv: Learning, 2019. 概 作者认为, adversarial
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摘要：Hu S, Yu T, Guo C, et al. A New Defense Against Adversarial Images: Turning a Weakness into a Strength[C]. neural information processing systems, 2019
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摘要：Mustafa A., Khan S., Hayat M., Goecke R., Shen J., Shao L., Adversarial Defense by Restricting the Hidden Space of Deep Neural Networks, arXiv preprin
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摘要：Ma X, Li B, Wang Y, et al. Characterizing Adversarial Subspaces Using Local Intrinsic Dimensionality[J]. arXiv: Learning, 2018. @article{ma2018charact
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摘要：Pang T, Du C, Zhu J, et al. Max-Mahalanobis Linear Discriminant Analysis Networks[C]. international conference on machine learning, 2018: 4013-4022. @
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摘要：Wang X., He K., Guo C., Weinberger K., Hopcroft H., AT-GAN: A Generative Attack Model for Adversarial Transferring on Generative Adversarial Nets. arX
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摘要：Wang Y, Zou D, Yi J, et al. Improving Adversarial Robustness Requires Revisiting Misclassified Examples[C]. international conference on learning repre
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摘要：Moosavidezfooli S, Fawzi A, Fawzi O, et al. Universal Adversarial Perturbations[C]. computer vision and pattern recognition, 2017: 86-94. @article{moo
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摘要：Schmidt L, Santurkar S, Tsipras D, et al. Adversarially Robust Generalization Requires More Data[C]. neural information processing systems, 2018: 5014
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摘要：Samangouei P, Kabkab M, Chellappa R, et al. Defense-GAN: Protecting Classifiers Against Adversarial Attacks Using Generative Models.[J]. arXiv: Comput
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摘要：Athalye A, Carlini N, Wagner D, et al. Obfuscated Gradients Give a False Sense of Security: Circumventing Defenses to Adversarial Examples[J]. arXiv:
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