Patch augmentation: Towards efficient decision boundaries for neural networks

Authors: Marcus D. Bloice, Peter M. Roth, Andreas Holzinger
Published: 2019-11-08 | Updated: 2019-11-25

Source: https://arxiv.org/abs/1911.07922

PDF: https://arxiv.org/pdf/1911.07922

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Abstract

In this paper we propose a new augmentation technique, called patch augmentation, that, in our experiments, improves model accuracy and makes networks more robust to adversarial attacks. In brief, this data-independent approach creates new image data based on image/label pairs, where a patch from one of the two images in the pair is superimposed on to the other image, creating a new augmented sample. The new image’s label is a linear combination of the image pair’s corresponding labels. Initial experiments show a several percentage point increase in accuracy on CIFAR-10, from a baseline of approximately 81 baseline of 52 also more robust to adversarial attacks, which we demonstrate using the Fast Gradient Sign Method.