SeMi: When Imbalanced Semi-Supervised Learning Meets Mining Hard Examples

• URL: http://arxiv.org/abs/2501.06004v1
• Date: Fri, 10 Jan 2025 14:35:16 GMT
• Title: SeMi: When Imbalanced Semi-Supervised Learning Meets Mining Hard Examples
• Authors: Yin Wang, Zixuan Wang, Hao Lu, Zhen Qin, Hailiang Zhao, Guanjie Cheng, Ge Su, Li Kuang, Mengchu Zhou, Shuiguang Deng,
• Abstract summary: Semi-Supervised Learning (SSL) can leverage abundant unlabeled data to boost model performance. The class-imbalanced data distribution in real-world scenarios poses great challenges to SSL, resulting in performance degradation. We propose a method that enhances the performance of Imbalanced Semi-Supervised Learning by Mining Hard Examples (SeMi)
• Score: 54.760757107700755
• License:
• Abstract: Semi-Supervised Learning (SSL) can leverage abundant unlabeled data to boost model performance. However, the class-imbalanced data distribution in real-world scenarios poses great challenges to SSL, resulting in performance degradation. Existing class-imbalanced semi-supervised learning (CISSL) methods mainly focus on rebalancing datasets but ignore the potential of using hard examples to enhance performance, making it difficult to fully harness the power of unlabeled data even with sophisticated algorithms. To address this issue, we propose a method that enhances the performance of Imbalanced Semi-Supervised Learning by Mining Hard Examples (SeMi). This method distinguishes the entropy differences among logits of hard and easy examples, thereby identifying hard examples and increasing the utility of unlabeled data, better addressing the imbalance problem in CISSL. In addition, we maintain a class-balanced memory bank with confidence decay for storing high-confidence embeddings to enhance the pseudo-labels' reliability. Although our method is simple, it is effective and seamlessly integrates with existing approaches. We perform comprehensive experiments on standard CISSL benchmarks and experimentally demonstrate that our proposed SeMi outperforms existing state-of-the-art methods on multiple benchmarks, especially in reversed scenarios, where our best result shows approximately a 54.8\% improvement over the baseline methods.

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