SeMi: When Imbalanced Semi-Supervised Learning Meets Mining Hard Examples
Zhejiang University（浙江大学）；The Hong Kong University of Science and Technology（香港科技大学）；Central South University（中南大学）；Zhejiang Gongshang University（浙江工商大学）
Yin Wang, Zixuan Wang, Hao Lu, Zhen Qin, Hailiang Zhao, Guanjie Cheng, Ge Su, Li Kuang, Mengchu Zhou, Shuiguang Deng
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.
Exploring "Many in Few" and "Few in Many" Properties in Long-Tailed, Highly-Imbalanced IC Defect Classification
Institute of Data Science and Information Computing, National Chung Hsing University（数据科学与信息计算研究所，中国台湾 Chung Hsing 大学）；Department of Electrical Engineering, National Tsing Hua University（电子工程系，中国台湾 Tsing Hua 大学）；Department of Electrical Engineering, National Taiwan University of Science and Technology（电子工程系，中国台湾 Science and Technology 大学）；Development of Smart Manufacturing, United Microelectronics Corporation（智能制造发展，联合微电子公司）
Long-Tailed Recognition Using Class-Balanced Experts
Max Planck Institute for Informatics（马克斯·普朗克信息学研究所）；Saarland Informatics Campus（萨尔兰信息学园区）；University of Maryland, College Park（马里兰大学帕克分校）；CISPA Helmholtz Center for Information Security（亥姆霍兹信息安全研究中心）
