  Advanced data augmentation strategies have widely been studied to improve the
generalization ability of deep learning models. Regional dropout is one of the
popular solutions that guides the model to focus on less discriminative parts
by randomly removing image regions, resulting in improved regularization.
However, such information removal is undesirable. On the other hand, recent
strategies suggest to randomly cut and mix patches and their labels among
training images, to enjoy the advantages of regional dropout without having any
pointless pixel in the augmented images. We argue that such random selection
strategies of the patches may not necessarily represent sufficient information
about the corresponding object and thereby mixing the labels according to that
uninformative patch enables the model to learn unexpected feature
representation. Therefore, we propose SaliencyMix that carefully selects a
representative image patch with the help of a saliency map and mixes this
indicative patch with the target image, thus leading the model to learn more
appropriate feature representation. SaliencyMix achieves the best known top-1
error of 21.26% and 20.09% for ResNet-50 and ResNet-101 architectures on
ImageNet classification, respectively, and also improves the model robustness
against adversarial perturbations. Furthermore, models that are trained with
SaliencyMix help to improve the object detection performance. Source code is
available at https://github.com/SaliencyMix/SaliencyMix.
