SuperMix: Supervising the Mixing Data Augmentation

Ali Dabouei

Abstract:

This paper presents a supervised mixing augmentation
method termed SuperMix, which exploits the salient regions
within input images to construct mixed training samples.
SuperMix is designed to obtain mixed images rich in visual
features and complying with realistic image priors. To enhance the efficiency of the algorithm, we develop a variant
of the Newton iterative method, 65× faster than gradient
descent on this problem. We validate the effectiveness of SuperMix through extensive evaluations and ablation studies
on two tasks of object classification and knowledge distillation. On the classification task, SuperMix provides comparable performance to the advanced augmentation methods, such as AutoAugment and RandAugment. In particular, combining SuperMix with RandAugment achieves
78.2% top-1 accuracy on ImageNet with ResNet50. On the
distillation task, solely classifying images mixed using the
teacher’s knowledge achieves comparable performance to
the state-of-the-art distillation methods. Furthermore, on
average, incorporating mixed images into the distillation
objective improves the performance by 3.4% and 3.1% on
CIFAR-100 and ImageNet, respectively. The code is available at https://github.com/alldbi/SuperMix.