Patch augmentation: Towards efficient decision boundaries for neural networks

Marcus Daniel Bloice, Peter M. Roth, Andreas Holzinger

Research output: Working paperPreprint


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.
Original languageEnglish
Publication statusPublished - 8 Nov 2019

Publication series e-Print archive
PublisherCornell University Library

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