解决长尾分布下的新类别发现难题，提升小样本类别的识别能力。
Generalized Category Discovery under the Long-Tailed Distribution
- 基于置信样本筛选与密度聚类的新型框架
- 在长尾与常规数据集上均显著优于现有方法
- 适合处理真实世界中类别不均衡的数据场景
本文研究长尾分布下的广义类别发现（GCD）问题，即利用已标注类别知识从无标签数据中发现新类别。现有工作假设数据分布均匀，但现实数据常呈长尾分布——少数类别占据多数样本，而多数类别仅含少量样本。尽管长尾分布已在监督和半监督学习中被广泛研究，但在GCD场景下仍属空白。我们识别出两个关键挑战：分类器学习的平衡性与类别数量估计。为此提出基于置信样本选择与密度聚类的框架。在长尾与传统GCD数据集上的实验表明，该方法有效提升了新类别发现性能。
原文摘要 · Abstract (English)
This paper addresses the problem of Generalized Category Discovery (GCD) under a long-tailed distribution, which involves discovering novel categories in an unlabelled dataset using knowledge from a set of labelled categories. Existing works assume a uniform distribution for both datasets, but real-world data often exhibits a long-tailed distribution, where a few categories contain most examples, while others have only a few. While the long-tailed distribution is well-studied in supervised and semi-supervised settings, it remains unexplored in the GCD context. We identify two challenges in this setting - balancing classifier learning and estimating category numbers - and propose a framework based on confident sample selection and density-based clustering to tackle them. Our experiments on both long-tailed and conventional GCD datasets demonstrate the effectiveness of our method.
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