Differences

This shows you the differences between two versions of the page.

Link to this comparison view

Both sides previous revision Previous revision
Next revision
Previous revision
adversarial_features [2018/03/05 20:53]
admin [Adversarial Features]
adversarial_features [2018/09/29 13:51] (current)
admin
Line 232: Line 232:
  
 https://​github.com/​locuslab/​convex_adversarial Provably robust neural networks https://​github.com/​locuslab/​convex_adversarial Provably robust neural networks
 +
 +https://​arxiv.org/​abs/​1711.10402v2 An Adversarial Neuro-Tensorial Approach For Learning Disentangled Representations
 +
 +We propose the first unsupervised deep learning method (with pseudo-supervision) for disentangling multiple latent factors of variation in face images captured in-the-wild.
 +
 +https://​arxiv.org/​abs/​1803.06373 Adversarial Logit Pairing
 +
 +When applied to clean examples and their adversarial counterparts,​ logit pairing improves accuracy on adversarial examples over vanilla adversarial training; we also find that logit pairing on clean examples only is competitive with adversarial training in terms of accuracy on two datasets.
 +
 +https://​github.com/​VishaalMK/​VectorDefense VectorDefense:​ Vectorization as a Defense to Adversarial Examples
 +
 +https://​arxiv.org/​pdf/​1805.04874.pdf GAN Q-learning
 +
 + In this paper, we propose
 +GAN Q-learning, a novel distributional RL method based on generative adversarial
 +networks (GANs) and analyse its performance in simple tabular environments,​
 +as well as OpenAI Gym. We empirically show that our algorithm leverages the
 +flexibility and blackbox approach of deep learning models while providing a viable
 +alternative to other state-of-the-art methods.
 +
 +https://​arxiv.org/​abs/​1805.12152v1 There Is No Free Lunch In Adversarial Robustness (But There Are Unexpected Benefits)
 +
 +
 + ​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