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• W2894770203 abstract "Deep networks often perform well on the data distribution on which they aretrained, yet give incorrect (and often very confident) answers when evaluatedon points from off of the training distribution. This is exemplified by theadversarial examples phenomenon but can also be seen in terms of modelgeneralization and domain shift. Ideally, a model would assign lower confidenceto points unlike those from the training distribution. We propose a regularizerwhich addresses this issue by training with interpolated hidden states andencouraging the classifier to be less confident at these points. Because thehidden states are learned, this has an important effect of encouraging thehidden states for a class to be concentrated in such a way so thatinterpolations within the same class or between two different classes do notintersect with the real data points from other classes. This has a majoradvantage in that it avoids the underfitting which can result frominterpolating in the input space. We prove that the exact condition for thisproblem of underfitting to be avoided by Manifold Mixup is that thedimensionality of the hidden states exceeds the number of classes, which isoften the case in practice. Additionally, this concentration can be seen asmaking the features in earlier layers more discriminative. We show that despiterequiring no significant additional computation, Manifold Mixup achieves largeimprovements over strong baselines in supervised learning, robustness tosingle-step adversarial attacks, semi-supervised learning, and NegativeLog-Likelihood on held out samples." @default.
• W2894770203 created "2018-10-12" @default.
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• W2894770203 date "2018-09-27" @default.
• W2894770203 modified "2023-09-27" @default.
• W2894770203 title "Manifold Mixup: Learning Better Representations by Interpolating Hidden States" @default.
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