The Mixup scheme suggests mixing a pair of samples to create an augmented
training sample and has gained considerable attention recently for improving
the generalizability of neural networks. A straightforward and widely used
extension of Mixup is to combine with regional dropout-like methods: removing
random patches from a sample and replacing it with the features from another
sample. Albeit their simplicity and effectiveness, these methods are prone to
create harmful samples due to their randomness. To address this issue, 'maximum
saliency' strategies were recently proposed: they select only the most
informative features to prevent such a phenomenon. However, they now suffer
from lack of sample diversification as they always deterministically select
regions with maximum saliency, injecting bias into the augmented data. In this
paper, we present, a novel, yet simple Mixup-variant that captures the best of
both worlds. Our idea is two-fold. By stochastically sampling the features and
'grafting' them onto another sample, our method effectively generates diverse
yet meaningful samples. Its second ingredient is to produce the label of the
grafted sample by mixing the labels in a saliency-calibrated fashion, which
rectifies supervision misguidance introduced by the random sampling procedure.
Our experiments under CIFAR, Tiny-ImageNet, and ImageNet datasets show that our
scheme outperforms the current state-of-the-art augmentation strategies not
only in terms of classification accuracy, but is also superior in coping under
stress conditions such as data corruption and object occlusion.

本文提出了一种新的混合方案，通过随机选择图像特征和标签之间的混合关系，生成多样化的样本，并使用最大显著性策略来校正随机采样引入的标签偏差，从而有效地提高了分类准确性。