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Adversarial Examples Are Not Bugs, They Are Features

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May 09 2019 Chris Olah

“Adversarial Examples Are Not Bugs, They Are Features” by Ilyas et al is pretty interesting. 📝Paper: https://arxiv.org/pdf/1905.02175.pdf 💻Blog: http://gradientscience.org/adv/ Some quick notes below.
3 replies, 712 likes


May 10 2019 Ilya Sutskever

https://arxiv.org/abs/1905.02175: a strange imagenet-like dataset with very wrong-looking labels, yet a model trained on it does totally well on the normal validation set. It's a crime against ML!
10 replies, 530 likes


May 09 2019 Lilian Weng

Two most interesting papers I’ve found recently: “the lottery ticket hypothesis” https://openreview.net/forum?id=rJl-b3RcF7 (probably already very famous) and “adversarial examples are not bugs but features” https://arxiv.org/abs/1905.02175
7 replies, 263 likes


May 08 2019 Louise Matsakis

Researchers from MIT now think adversarial examples aren’t AI “hallucinations” after all. The classifier is just “seeing” things that humans can’t. It’s really interesting work! https://www.wired.com/story/adversarial-examples-ai-may-not-hallucinate/
2 replies, 121 likes


May 10 2019 Wojciech Zaremba

Adversarial examples are great features. There is nothing wrong with them. We are just blind to them. Wow. https://arxiv.org/pdf/1905.02175.pdf
1 replies, 89 likes


May 08 2019 Aleksander Madry

A great piece by @lmatsakis about our recent work on how adversarial examples are actually helpful features! To read more see: http://gradientscience.org/adv and the paper is here: https://arxiv.org/abs/1905.02175.
2 replies, 36 likes


Aug 23 2019 Hamid Eghbal-zadeh

So https://arxiv.org/abs/1905.02175 says we can use adversarial examples to train models that generalise. But https://openreview.net/forum?id=rJMw747l_4 says if you train a model with samples from a GAN, it does not generalise, unless you mix it up with real samples!🤔
2 replies, 29 likes


Jun 04 2019 Shreya Shankar

does this stuff interest you? some good papers (in my opinion): * Motivating the Rules of the Game for Adversarial Example Research: https://arxiv.org/abs/1807.06732 (Gilmer et al. 2018) * Adversarial Examples Are Not Bugs, They Are Features: https://arxiv.org/abs/1905.02175 (Ilyas et al. 2019)
1 replies, 28 likes


May 07 2019 Thomas Lahore

Adversarial Examples Are Not Bugs, They Are Features "adversarial examples can be directly attributed to the presence of non-robust features ... patterns in the data distribution that are highly predictive, yet brittle and incomprehensible to humans" https://arxiv.org/abs/1905.02175
0 replies, 24 likes


May 09 2019 Adam J Calhoun

What if adversarial examples exist because they help the network generalize? And it is simply our puny human minds that aren't able to understand that? https://arxiv.org/abs/1905.02175 https://t.co/JPvNWCjqZG
0 replies, 11 likes


Jul 02 2019 Daisuke Okanohara

Adversarial examples are not the result of artifacts or overfitting. It is because the learned classifier captures "non-robust" features, which are not incomprehensible by humans but are actually predictive and generalizable. https://arxiv.org/abs/1905.02175
0 replies, 11 likes


May 07 2019 午後のarXiv

"Adversarial Examples Are Not Bugs, They Are Features", Andrew Ilyas, Shibani Santurkar, Dimitris Tsipras, Logan En… https://arxiv.org/abs/1905.02175
0 replies, 7 likes


May 10 2019 Alexander Novikov

Cool! Like furry ear is a feature which can be used to detect cats, adversarial perturbations are features of natural images which can be used to correctly classify both train and test data, except humans don't see it. So adversarial perturbations are human's bugs, not model's.
0 replies, 6 likes


Jun 10 2019 Stefanie Sirén-Heikel

What if adversarial examples aren't bugs, but features? Check out the story on how we might actually really misunderstand how machine learning systems work: https://www.wired.com/story/adversarial-examples-ai-may-not-hallucinate/ and read the paper here: https://arxiv.org/abs/1905.02175
1 replies, 4 likes


May 11 2019 Bobby Filar

"Adversarial Examples Are Not Bugs, They Are Features" by @andrew_ilyas, @tsiprasd et al. https://arxiv.org/pdf/1905.02175.pdf
0 replies, 3 likes


May 10 2019 Eric Silverman

This is a really cool paper! Adversarial examples are getting a lot of attention in deep learning research and this paper offers some real insight into the issue
0 replies, 2 likes


May 10 2019 @reiver ⊼ (Charles Iliya Krempeaux)

"Adversarial Examples Are Not Bugs, They Are Features" by Andrew Ilyas, Shibani Santurkar, Dimitris Tsipras, Logan Engstrom, Brandon Tran, Aleksander Madry https://arxiv.org/abs/1905.02175 (machine learning) H/T @QEDanMazur
0 replies, 2 likes


May 09 2019 Hacker News

Adversarial Examples Are Not Bugs, They Are Features: https://arxiv.org/abs/1905.02175 Comments: https://news.ycombinator.com/item?id=19863913
0 replies, 1 likes


May 09 2019 Hacker News 20

Adversarial Examples Are Not Bugs, They Are Features https://arxiv.org/abs/1905.02175 (http://bit.ly/2YimheO)
0 replies, 1 likes


May 31 2019 Benjamin Singleton

Adversarial Examples Are Not Bugs, They Are Features #DataScience #BigData https://arxiv.org/abs/1905.02175
0 replies, 1 likes


May 13 2019 Wilfred Hughes

Really interesting paper exploring adversarial inputs to ML models: https://arxiv.org/abs/1905.02175 They conclude: * It's a property of the input data, not the training * You can even train a model on non-robust features and obtain a model that works well on the original input data!
0 replies, 1 likes


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