Class-Adaptive Data Augmentation for Image Classification

Abstract

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. INDEX TERMS image classification, data augmentation, class-adaptive data augmentation, hyperparameter optimizationRecently, attempts have been made to automatically search for an appropriate augmentation policy for a given training dataset in a data-driven manner [10][11][12][13][14][15]. Existing methods can be used to effectively apply data augmentation to improve the performance of a CNN in the absence of domain knowledge. These methods mainly focus on optimizing the augmentation policy to be dataset-specific, implying that every image in the dataset is randomly transformed in the same manner, regardless of the class label.Our research motivation stems from the fact that appropriate augmentation policies can differ between classes. For example, in the digit images of MNIST and SVHN datasets, random horizontal and vertical flips preserve the class labels

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