On Adversarial Risk and Training
In this work we formally define the notions of adversarial perturbations, adversarial risk and adversarial training and analyze their properties. Our analysis provides several interesting insights into adversarial risk, adversarial training, and their relation to the classification risk, "traditional" training. We also show that adversarial training can result in models with better classification accuracy and can result in better explainable models than traditional training. Although adversarial training is computationally expensive, our results and insights suggest that one should prefer adversarial training over traditional risk minimization for learning complex models from data.
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Arun Sai Suggala (add twitter)
Adarsh Prasad (add twitter)
Vaishnavh Nagarajan (add twitter)
Pradeep Ravikumar (add twitter)
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07/01/18 06:25PM
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nmfeeds: [O] https://t.co/4N5Dij9AP5 On Adversarial Risk and Training. Recent works on adversarial perturbations show that there is...
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