Jing Chai

Distribution-aware probability contrastive learning for class-imbalanced semi-supervised learning

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.