Lecuyer M, Atlidakis V, Geambasu R, et al. Certified Robustness to Adversarial Examples...
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Certified Robustness to Adversarial Examples with Differential Privacy
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Dynamic Routing Between Capsules
Sabour S, Frosst N, Hinton G E, et al. Dynamic Routing Between Capsules[C]. neural info...
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Defending Adversarial Attacks by Correcting logits
Li Y., Xie L., Zhang Y., Zhang R., Wang Y., Tian Q., Defending Adversarial Attacks by C...
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Visualizing Data using t-SNE
Der Maaten L V, Hinton G E. Visualizing data using t-SNE[J]. Journal of Machine Learnin...
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An Introduction to Measure Theory and Probability
Luigi Ambrosio, Giuseppe Da Prato, Andrea Mennucci, An Introduction to Measure Theory a...
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BAIRE ONE FUNCTIONS (Baire第一類函數(shù))
[TOC] JOHNNY HU, BAIRE ONE FUNCTIONS. 一些基本的定義(諸如逐點收斂, 一致收斂集合等)就不敘述了. 定義 Definition: 令, ...
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Inverse/Implicit Function Theorem
@[TOC] Chapter 4 Inverse Function Theorem 這個章節(jié)講得很好, 還引用了莊子秋水中的一段話, 大佬啊. 4.1 The Inverse...
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A New Defense Against Adversarial Images: Turning a Weakness into a Strength
Hu S, Yu T, Guo C, et al. A New Defense Against Adversarial Images: Turning a Weakness ...
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Adversarial Defense by Restricting the Hidden Space of Deep Neural Networks
Mustafa A., Khan S., Hayat M., Goecke R., Shen J., Shao L., Adversarial Defense by Rest...
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CHARACTERIZING ADVERSARIAL SUBSPACES USING LOCAL INTRINSIC DIMENSIONALITY
Ma X, Li B, Wang Y, et al. Characterizing Adversarial Subspaces Using Local Intrinsic D...
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cosface: large margin cosine loss for deep face recognition
Wang H, Wang Y, Zhou Z, et al. CosFace: Large Margin Cosine Loss for Deep Face Recognit...
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Max-Mahalanobis Linear Discriminant Analysis Networks
Pang T, Du C, Zhu J, et al. Max-Mahalanobis Linear Discriminant Analysis Networks[C]. i...
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AT-GAN: A Generative Attack Model for Adversarial Transferring on Generative Adversarial Nets
[TOC] Wang X., He K., Guo C., Weinberger K., Hopcroft H., AT-GAN: A Generative Attack M...
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Generating Adversarial Examples with Adversarial Networks
Xiao C, Li B, Zhu J, et al. Generating Adversarial Examples with Adversarial Networks[J...
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Universal adversarial perturbations
Moosavidezfooli S, Fawzi A, Fawzi O, et al. Universal Adversarial Perturbations[C]. com...
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ADAM : A METHOD FOR STOCHASTIC OPTIMIZATION
Kingma D P, Ba J. Adam: A Method for Stochastic Optimization[J]. arXiv: Learning, 2014....
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IMPROVING ADVERSARIAL ROBUSTNESS REQUIRES REVISITING MISCLASSIFIED EXAMPLES
Wang Y, Zou D, Yi J, et al. Improving Adversarial Robustness Requires Revisiting Miscla...
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Adversarially Robust Generalization Requires More Data
Schmidt L, Santurkar S, Tsipras D, et al. Adversarially Robust Generalization Requires ...
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DEFENSE-GAN: PROTECTING CLASSIFIERS AGAINST ADVERSARIAL ATTACKS USING GENERATIVE MODELS
Samangouei P, Kabkab M, Chellappa R, et al. Defense-GAN: Protecting Classifiers Against...
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Obfuscated Gradients Give a False Sense of Security: Circumventing Defenses to Adversarial Examples
Athalye A, Carlini N, Wagner D, et al. Obfuscated Gradients Give a False Sense of Secur...