Distribution-aware probability contrastive learning for class-imbalanced semi-supervised learning
TL;DR
DPCL was leveraged to learn better representation distributions by uniformly projecting data on a hypersphere, and by which more accurate pseudo labels could be generated, and achieves consistent improvements over existing state-of-the-art methods across multiple benchmarks.
Abstract
Semi-Supervised Learning (SSL) has shown significant advantages by leveraging abundant unlabeled data to enhance model performance with successful applications in computer vision. However, existing SSL methods might exhibit significant performance degradation in real applications, primarily due to the learning bias stemming from the following two challenges: (1) the occurrence of class imbalance in real-world datasets, and (2) the misalignment between class distributions of labeled and unlabeled data. To deal with the above challenges, current Class-Imbalanced SSL (CISSL) methods mainly resorted to data rebalancing strategies (e.g., resampling) and failed to fully exploit the high-level distributional characteristics. To address this problem, a novel solution termed Distribution-Aware Probability Contrastive Learning (DPCL) is proposed in this work. In DPCL, contrastive learning was leveraged to learn better representation distributions by uniformly projecting data on a hypersphere, and by which more accurate pseudo labels could be generated. To evaluate the classification performance of DPCL, we conduct extensive experiments on benchmark class-imbalanced SSL datasets. The results demonstrate that DPCL achieves consistent improvements over existing state-of-the-art methods across multiple benchmarks.