Bibliographic Information: Zhou, Z.-H., Fang, S., Zhou, Z.-J., Wei, T., Wan, Y., & Zhang, M.-L. (2024). Continuous Contrastive Learning for Long-Tailed Semi-Supervised Recognition. In Proceedings of the 38th Conference on Neural Information Processing Systems (NeurIPS 2024).
Research Objective: This paper aims to address the challenges of long-tailed semi-supervised learning, where labeled data is scarce and exhibits a long-tailed distribution, leading to biased pseudo-label generation and hindering model performance.
Methodology: The authors propose a novel probabilistic framework that unifies various recent long-tail learning proposals and introduces Continuous Contrastive Learning (CCL). CCL extends class-balanced contrastive learning to LTSSL by utilizing "continuous pseudo-labels" derived from model predictions and propagated labels. It employs a dual-branch training strategy, where one branch focuses on balanced classification and the other on standard training. Additionally, CCL incorporates energy score-based data selection to mitigate confirmation bias and improve the quality of learned representations.
Key Findings: Extensive experiments on CIFAR10-LT, CIFAR100-LT, STL10-LT, and ImageNet-127 datasets demonstrate that CCL consistently outperforms previous state-of-the-art methods in both consistent (γl = γu) and inconsistent (γl ≠ γu) settings. Notably, CCL achieves significant performance gains, particularly on highly imbalanced datasets, highlighting the effectiveness of its probabilistic framework and continuous pseudo-label utilization for representation learning.
Main Conclusions: The authors conclude that CCL effectively addresses the challenges of LTSSL by unifying existing long-tail learning methods within a probabilistic framework and leveraging continuous pseudo-labels for improved representation learning. The proposed method demonstrates superior performance compared to existing approaches, particularly in handling imbalanced datasets with varying label distributions.
Significance: This research significantly contributes to the field of LTSSL by providing a novel and effective method for handling imbalanced datasets, which are prevalent in real-world applications. The proposed CCL method and its underlying probabilistic framework offer valuable insights for future research in LTSSL and related areas.
Limitations and Future Research: While CCL demonstrates promising results, the authors acknowledge limitations regarding computational complexity and the potential for further improvement in pseudo-label quality. Future research directions include exploring more efficient implementations of CCL and investigating advanced techniques for generating highly reliable pseudo-labels in LTSSL.
他の言語に翻訳
原文コンテンツから
arxiv.org
深掘り質問