用等角超球点做类别原型，让选样本更均衡多样。
Maximally Separated Active Learning
- 用固定等角超球点做类原型，保证类别间分离度
- 在5个数据集上优于现有方法，减少标注成本
- 无需聚类，易集成，适合追求高效标注的场景
主动学习通过从无标签数据中选择最具有信息量的样本，以最小化标注成本的同时优化模型性能。传统不确定性采样常因选取相似的不确定样本而产生采样偏差。本文提出一种新方法，利用固定的等角超球点作为类别原型，确保类别间一致的分离性与鲁棒的特征表示。所提方法名为最大分离主动学习（MSAL），用于不确定性采样，并设计了结合多样性的策略（MSAL-D）。该方法无需昂贵的聚类步骤，通过超球面均匀性维持多样性。我们在五个基准数据集上验证了其优越性能，凸显方法的有效性与易集成性。代码已开源。
原文摘要 · Abstract (English)
Active Learning aims to optimize performance while minimizing annotation costs by selecting the most informative samples from an unlabelled pool. Traditional uncertainty sampling often leads to sampling bias by choosing similar uncertain samples. We propose an active learning method that utilizes fixed equiangular hyperspherical points as class prototypes, ensuring consistent inter-class separation and robust feature representations. Our approach introduces Maximally Separated Active Learning (MSAL) for uncertainty sampling and a combined strategy (MSAL-D) for incorporating diversity. This method eliminates the need for costly clustering steps, while maintaining diversity through hyperspherical uniformity. We demonstrate strong performance over existing active learning techniques across five benchmark datasets, highlighting the method's effectiveness and integration ease. The code is available on GitHub.
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