Stackmix: a complementary mix algorithm

Techniques combining multiple images as input/output have proven to be effective data augmentations for training convolutional neural networks. In this paper, we present StackMix: each input is presented as a concatenation of two images, and the label is the mean of the two one-hot labels. On its own, StackMix rivals other widely used methods in the “Mix” line of work. More importantly, unlike previous work, significant gains across a variety of benchmarks are achieved by combining StackMix with existing Mix augmentation, effectively mixing more than two images. E. g. , by combining StackMix with CutMix, test error in the supervised setting is improved across a variety of settings over CutMix, including 0. 8% on ImageNet, 3% on Tiny ImageNet, 2% on CIFAR-100, 0. 5% on CIFAR-10, and 1. 5% on STL-10. Similar results are achieved with Mixup. We further show that gains hold for robustness to common input corruptions and perturbations at varying severities with a 0. 7% improvement on CIFAR-100-C, by combining StackMix with AugMix over AugMix. On its own, improvements with StackMix hold across different number of labeled samples on CIFAR-100, maintaining approximately a 2% gap in test accuracy –down to using only 5% of the whole dataset– and is effective in the semi-supervised setting with a 2% improvement with the standard benchmark Pi-model. Finally, we perform an extensive ablation study to better understand the proposed methodology.

Authors

• John Chen 0002
• Samarth Sinha
• Anastasios Kyrillidis

Keywords

• Data augmentation
• Deep Learning