Geometric imbalanced deep learning with feature scaling and boundary sample mining

Abstract

Data imbalance is a significant factor affecting classification performance in computer vision. In particular, data imbalance is harmful to classification learning and representation learning. To address this issue, this paper proposes a geometric deep learning framework combined with Feature Scaling Module (FSM) and Boundary Samples Mining Module (BSMM). Considering the geometric information in sample distributions of training samples, FSM is proposed to scale the features by hypersphere radius of each class, which improves the representation ability of minority classes. Meanwhile, it is noteworthy that the relationships and information between samples are essential for classification. Therefore, BSMM is proposed to mine the boundary samples by Gabriel Graph that takes the relationships into account. Finally, a loss scheduler is designed to adjust the training process of these two modules. With the scheduler, the model first learns representation and then focuses more on minority classes gradually. Extensive experiments on three benchmark datasets demonstrate the advantages of the proposed learning framework over the state-of-the-art models for solving the imbalance problem.