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,
$65\times$ 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. {\it The code is available at
this https URL}.

本文提出了一种监督式混合增强方法 SuperMix，使用牛顿迭代方法来提高效率，在对象分类和知识蒸馏任务中通过广泛的评估和消融研究验证了其有效性，将混合图像纳入蒸馏目标可显著提高结果。