Research based on Mixed Sample Data Augmentation method

Abstract

Mixed Sample Data Augmentation (MSDA) has flourished in recent years, with many successful methods. These approaches have greatly alleviated neural network problems due to overfitting, class imbalance, adversarial attacks, etc. However, most of these methods fail to distinguish between image style and content, which causes the network to be biased to focus on a small subset of information to learn the features of the category. To further improve the performance of the model, this paper proposes a new method based on MSDA. This approach not only generates samples that contain more information at larger spatial scales, but also enables the network to manipulate the content and style information of input image pairs separately by generating samples that transfer their styles. Through extensive experiments, we compare the performance of previous approaches, and this approach has been shown to improve the classification accuracy of the model and make the model more general and robust.