RandomMix: A mixed sample data augmentation method with multiple mixed modes

Data augmentation is a very practical technique that can be used to improve the generalization ability of neural networks and prevent overfitting. Recently, mixed sample data augmentation has received a lot of attention and achieved great success. In order to enhance the performance of mixed sample data augmentation, a series of recent works are devoted to obtaining and analyzing the salient regions of the image, and using the saliency area to guide the image mixing. However, obtaining the salient information of an image requires a lot of extra calculations. Different from improving performance through saliency analysis, our proposed method RandomMix mainly increases the diversity of the mixed sample to enhance the generalization ability and performance of neural networks. Moreover, RandomMix can improve the robustness of the model, does not require too much additional calculation, and is easy to insert into the training pipeline. Finally, experiments on the CIFAR-10/100, Tiny-ImageNet, ImageNet, and Google Speech Commands datasets demonstrate that RandomMix achieves better performance than other state-of-the-art mixed sample data augmentation methods.