Retrieval-Augmented Convolutional Neural Networks for Improved Robustness against Adversarial Examples

Jake Zhao; Kyunghyun Cho

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arXiv · 1802.09502

Retrieval-Augmented Convolutional Neural Networks for Improved Robustness against Adversarial Examples

Jake Zhao·Kyunghyun Cho

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Abstract

We propose a retrieval-augmented convolutional network and propose to train it with local mixup, a novel variant of the recently proposed mixup algorithm. The proposed hybrid architecture combining a convolutional network and an off-the-shelf retrieval engine was designed to mitigate the adverse effect of off-manifold adversarial examples, while the proposed local mixup addresses on-manifold ones by explicitly encouraging the classifier to locally behave linearly on the data manifold. Our evaluation of the proposed approach against five readily-available adversarial attacks on three datasets--CIFAR-10, SVHN and ImageNet--demonstrate the improved robustness compared to the vanilla convolutional network.

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Jake Zhao, Kyunghyun Cho. 2018-02-26. Retrieval-Augmented Convolutional Neural Networks for Improved Robustness against Adversarial Examples. https://arxiv.org/abs/1802.09502

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