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- W2807148750 abstract "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, 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." @default.
- W2807148750 created "2018-06-13" @default.
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- W2807148750 date "2018-06-07" @default.
- W2807148750 modified "2023-09-27" @default.
- W2807148750 title "On Adversarial Risk and Training." @default.
- W2807148750 hasPublicationYear "2018" @default.
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