Similar classes latent distribution modelling-based oversampling method for imbalanced image classification

Abstract

Learning an unbiased classifier from imbalanced image datasets is challenging since the classifier may be strongly biased toward the majority class. To address this issue, some generative model-based oversampling methods have been proposed. However, most of these methods pay little attention to boundary samples, which may contribute tiny to learning an unbiased classifier. In this paper, we focus on boundary samples and propose a similar classes latent distribution modelling-based oversampling method. Specifically, first, we model each class as different von Mises–Fisher distributions, thereby aligning feature learning with the class distributions. Furthermore, we develop a distance minimization loss function, which makes latent representations from similar classes close to each other. In this way, the generator can capture more shared features during training. In addition, we propose a boundary sampling strategy, which uses latent variables near the decision boundary to generate boundary samples. These samples expand the minority decision region and reshape the decision boundary. Experiments on four imbalanced image datasets show that the proposed method achieves promising performance in terms of Recall, Precision, F1-score, and G-mean.