Continuous Contrastive Learning for Long-Tailed Semi-Supervised Recognition
<|reference_start|>Continuous Contrastive Learning for Long-Tailed Semi-Supervised Recognition: Long-tailed semi-supervised learning poses a significant challenge in training models with limited labeled data exhibiting a long-tailed label distribution. Current state-of-the-art LTSSL approaches heavily rely on high-quality pseudo-labels for large-scale unlabeled data. However, these methods often neglect the impact of representations learned by the neural network and struggle with real-world unlabeled data, which typically follows a different distribution than labeled data. This paper introduces a novel probabilistic framework that unifies various recent proposals in long-tail learning. Our framework derives the class-balanced contrastive loss through Gaussian kernel density estimation. We introduce a continuous contrastive learning method, CCL, extending our framework to unlabeled data using reliable and smoothed pseudo-labels. By progressively estimating the underlying label distribution and optimizing its alignment with model predictions, we tackle the diverse distribution of unlabeled data in real-world scenarios. Extensive experiments across multiple datasets with varying unlabeled data distributions demonstrate that CCL consistently outperforms prior state-of-the-art methods, achieving over 4% improvement on the ImageNet-127 dataset. Our source code is available at https://github.com/zhouzihao11/CCL<|reference_end|>
@article{zhou2024continuous,
    title={Continuous Contrastive Learning for Long-Tailed Semi-Supervised
  Recognition},
    author={Zi-Hao Zhou and Siyuan Fang and Zi-Jing Zhou and Tong Wei and Yuanyu
  Wan and Min-Ling Zhang},
    journal={arXiv preprint arXiv:2410.06109},
    year={2024},
    archivePrefix={arXiv},
    eprint={2410.06109},
    primaryClass={cs.LG}
}