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- W4287596593 abstract "Recent evidence shows that convolutional neural networks (CNNs) are biased towards textures so that CNNs are non-robust to adversarial perturbations over textures, while traditional robust visual features like SIFT (scale-invariant feature transforms) are designed to be robust across a substantial range of affine distortion, addition of noise, etc with the mimic of human perception nature. This paper aims to leverage good properties of SIFT to renovate CNN architectures towards better accuracy and robustness. We borrow the scale-space extreme value idea from SIFT, and propose extreme value preserving networks (EVPNets). Experiments demonstrate that EVPNets can achieve similar or better accuracy than conventional CNNs, while achieving much better robustness on a set of adversarial attacks (FGSM,PGD,etc) even without adversarial training." @default.
- W4287596593 created "2022-07-25" @default.
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- W4287596593 date "2020-11-16" @default.
- W4287596593 modified "2023-10-16" @default.
- W4287596593 title "Extreme Value Preserving Networks" @default.
- W4287596593 hasPublicationYear "2020" @default.
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