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[R] Adversarial Examples Aren’t Bugs, They’re Features

Blog post: http://gradientscience.org/adv

Paper: https://arxiv.org/abs/1905.02175

Hi, I’m one of the lead authors on this paper.

TL;DR: We show that adversarial examples aren’t really weird aberrations or random artifacts, and are instead actually meaningful but imperceptible features of the data distribution (i.e. they are helpful for generalization). We prove this through a series of experiments that shows that (a) you can learn just based on these imperceptible features embedded into a completely mislabeled training set and generalize to the true test set (b) you can remove these imperceptible features and generalize *robustly* to the true test set (with standard training).

We would love to answer any questions/comments!

submitted by /u/andrew_ilyas
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Toronto AI is a social and collaborative hub to unite AI innovators of Toronto and surrounding areas. We explore AI technologies in digital art and music, healthcare, marketing, fintech, vr, robotics and more. Toronto AI was founded by Dave MacDonald and Patrick O'Mara.