  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.
