Circular Shift: An Effective Data Augmentation Method For Convolutional Neural Network On Image Classification
Kailai Zhang, Zheng Cao, Ji Wu
Abstract
In this paper, we present a novel and effective data augmentation method for convolutional neural network(CNN) on image classification tasks. CNN-based models such as VGG, Resnet and Densenet have achieved great success on image classification tasks. The common data augmentation methods such as rotation, crop and flip are always used for CNN, especially under the lack of data. However, in some cases such as small images and dispersed feature of objects, these methods have limitations and even can decrease the classification performance. In this case, an operation that has lower risk is important for the performance improvement. Addressing this problem, we design a data augmentation method named circular shift, which provides variations for the CNN-based models but does not lose too much information. Three commonly used image datasets are chosen for the evaluation of our proposed operation, and the experiment results show consistent improvement on different CNN-based models. What is more, our operation can be added to the current set of augmentation operation and achieves further performance improvement.