GuidedMixup: An Efficient Mixup Strategy Guided by Saliency Maps
Minsoo Kang Suhyun Kim
Data augmentation is now an essential part of the image training process, as
it effectively prevents overfitting and makes the model more robust against
noisy datasets. Recent mixing augmentation strategies have advanced to generate
the mixup mask that can enrich the saliency information, which is a supervisory
signal. However, these methods incur a significant computational burden to
optimize the mixup mask. From this motivation, we propose a novel
saliency-aware mixup method, GuidedMixup, which aims to retain the salient
regions in mixup images with low computational overhead. We develop an
efficient pairing algorithm that pursues to minimize the conflict of salient
regions of paired images and achieve rich saliency in mixup images. Moreover,
GuidedMixup controls the mixup ratio for each pixel to better preserve the
salient region by interpolating two paired images smoothly. The experiments on
several datasets demonstrate that GuidedMixup provides a good trade-off between
augmentation overhead and generalization performance on classification
datasets. In addition, our method shows good performance in experiments with
corrupted or reduced datasets.