Patch augmentation: Towards efficient decision boundaries for neural networks

Marcus D. Bloice; Peter M. Roth; Andreas Holzinger

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arXiv · 1911.07922

Patch augmentation: Towards efficient decision boundaries for neural networks

Marcus D. Bloice·Peter M. Roth·Andreas Holzinger

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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% to 89%. CIFAR-100 sees larger improvements still, from a baseline of 52% to 68% accuracy. Networks trained using patch augmentation are also more robust to adversarial attacks, which we demonstrate using the Fast Gradient Sign Method.

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Marcus D. Bloice, Peter M. Roth, Andreas Holzinger. 2019-11-08. Patch augmentation: Towards efficient decision boundaries for neural networks. https://arxiv.org/abs/1911.07922

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