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

arXiv: https://arxiv.org/abs/1802.09502 · HTML: https://mlsanity.com/paper/1802.09502/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Feb 2018.

## Built on by (most cited first, 2 of 2)

- [TabR: Tabular Deep Learning Meets Nearest Neighbors in 2023](https://mlsanity.com/paper/2307.14338/) (2023)
- [ROMO: Retrieval-enhanced Offline Model-based Optimization](https://mlsanity.com/paper/2310.07560/) (2023)

## Abstract (arXiv)

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.