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adversarial_features [2018/06/01 19:48]
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adversarial_features [2018/09/29 13:51] (current)
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  ​Robust models turn out to have interpretable gradients and feature representations that align unusually well with salient data characteristics. In fact, they yield striking feature interpolations that have thus far been possible to obtain only using generative models such as GANs.  ​Robust models turn out to have interpretable gradients and feature representations that align unusually well with salient data characteristics. In fact, they yield striking feature interpolations that have thus far been possible to obtain only using generative models such as GANs.
 +
 +https://​arxiv.org/​abs/​1806.06108v1 Non-Negative Networks Against Adversarial Attacks
 +
 +https://​arxiv.org/​pdf/​1805.12177v1.pdf Why do deep convolutional networks generalize so
 +poorly to small image transformations?​
 +
 +https://​arxiv.org/​abs/​1806.11146 Adversarial Reprogramming of Neural Networks
 +
 +https://​github.com/​anishathalye/​obfuscated-gradients
 +
 +https://​arxiv.org/​pdf/​1805.12152.pdf There Is No Free Lunch In Adversarial Robustness
 +(But There Are Unexpected Benefits)
 +An Intriguing Failing of Convolutional Neural Networks and the CoordConv Solution
 +
 +https://​arxiv.org/​abs/​1807.03247 An Intriguing Failing of Convolutional Neural Networks and the CoordConv Solution
 +
 +
 +https://​arxiv.org/​abs/​1808.03305The Elephant in the Room
 +
 +https://​openreview.net/​pdf?​id=S1xoy3CcYX ADVERSARIAL EXAMPLES ARE A NATURAL CONSEQUENCE
 +OF TEST ERROR IN NOISE
 +