Class-Adaptive Data Augmentation for Image Classification

Data augmentation is a widely used regularization technique for improving the performance of convolutional neural networks (CNNs) in image classification tasks. To improve the effectiveness of data augmentation, it is important to find label-preserving transformations that fit the domain knowledge for a given dataset. In several real-world datasets, appropriate augmentation policies differ between classes, owing to their different characteristics. In this paper, we propose a class-adaptive data augmentation method that utilizes class-specific augmentation policies. First, we train the CNN without data augmentation. Subsequently, we derive a suitable augmentation policy for each class through an optimization procedure to maximize the degree of transformation while maintaining the label-preserving property of CNNs. Finally, we re-train the model using data augmentation based on derived class-specific augmentation policies. Through experiments using benchmark datasets with class-specific transformation constraints, we demonstrate that the proposed method achieves comparable or higher classification accuracy than the baseline methods using the same augmentation policy for all classes. Additionally, we confirm that the derived class-specific augmentation policies are consistent with the domain knowledge of each dataset.