CyclicShift: A Data Augmentation Method For Enriching Data Patterns

Abstract

In this paper, we propose a simple yet effective data augmentation strategy, dubbed CyclicShift, to enrich data patterns. The idea is to shift the image in a certain direction and then circularly refill the resultant out-of-frame part to the other side. Compared with previous related methods, Translation, and Shuffle, our proposed method is able to avoid losing pixels of the original image and preserve its semantic information as much as possible. Visually and emprically, we show that our method indeed brings new data patterns and thereby improves the generalization ability as well as the performance of models. Extensive experiments demonstrate our method's effectiveness in image classification and fine-grained recognition over multiple datasets and various network architectures. Furthermore, our method can also be superimposed on other data augmentation methods in a very simple way. CyclicMix, the simultaneous use of CyclicShift and CutMix, hits a new high in most cases. Our code is open-source and available at https://github.com/dejavunHui/CyclicShift.