# Retrieval-Augmented Convolutional Neural Networks for Improved Robustness against Adversarial Examples Source: [Retrieval-Augmented Convolutional Neural Networks for Improved Robustness against Adversarial Examples]() Collection index: [llms.txt]() Data updated: 2025-10-25T20:56:11.088Z Authors: Jake Zhao, Kyunghyun Cho Published: 2018-02-26T18:29:20.000Z ## 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. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()