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There's a good summary of the paper by the authors here, for people who don't want to digest the pdf: http://gradientscience.org/adv/ http://gradientscience.org
by Chirono 7y ago
There's a good summary of the paper by the authors here, for people who don't want to digest the pdf: http://gradientscience.org/adv/ http://gradientscience.org/adv/
- andybak 7y agoI find academic papers fairly indigestible both because of their language and verbosity and because PDF is a fairly horrible format for reading on screen. So thank you.
- p1esk 7y agoThey get easier to digest after you’ve read a few dozen. Or written a couple.
- gambler 7y ago>Our discussion and experiments establish adversarial examples as a purely human-centric phenomenon. Can someone explain to me the difference between this statement and the notion that AI performance in general is completely subjective? We're trying to train models to do certain things. It doesn't matter if you call them human-centric or not. The important thing is that we have a goal for training. Adversarial examples force models to do other things, i.e. behave in a way that defies the original goal. How is saying "no, misclassifying those bad examples is okay" different from moving the goalposts? Their process of retraining doesn't make any sense to me either. So what if the model trained on mislabeled data has some degree of accuracy on real data? This just shows that there is some internal symmetry involved.