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-theshelf 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.
REF augments the convolutional network with an off-the-shelf image retrieval system to mitigate the adverse effect of "off-manifold" adversarial examples, and uses local mixup to increase robustness to "onmanifold" adversarial examples.
Retrieval-Augmented Convolutional Neural Networks for Improved Robustness against Adversarial Examples
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