用更少标注数据提升模型性能，让机器学习更高效。
Active Learning Methods for Efficient Data Utilization and Model Performance Enhancement
- 通过主动选择最有价值的数据进行标注，减少对大量标签的依赖。
- 在计算机视觉与自然语言处理中，显著优于传统被动学习方法。
- 适合数据标注成本高、样本稀缺的场景，如医疗图像分析。
在数据驱动智能时代，数据丰富与标注稀缺的矛盾已成为机器学习发展的关键瓶颈。本文详细综述了主动学习（Active Learning, AL）策略，该策略通过仅使用少量标注样本来实现模型性能的提升。文章介绍了AL的基本概念，并探讨其在计算机视觉、自然语言处理、迁移学习及实际应用中的广泛用途。重点讨论了不确定性估计、类别不平衡处理、领域自适应、公平性以及强评估指标和基准构建等重要研究方向。研究表明，模拟人类提问式的学习方式可显著提高数据利用效率，使模型更有效学习。同时指出当前面临的挑战，包括重建信任、确保可复现性及解决方法不一致等问题。实验表明，在使用良好评估标准时，主动学习通常优于被动学习。本文旨在为研究人员与实践者提供关键洞见，并提出未来发展方向。
原文摘要 · Abstract (English)
In the era of data-driven intelligence, the paradox of data abundance and annotation scarcity has emerged as a critical bottleneck in the advancement of machine learning. This paper gives a detailed overview of Active Learning (AL), which is a strategy in machine learning that helps models achieve better performance using fewer labeled examples. It introduces the basic concepts of AL and discusses how it is used in various fields such as computer vision, natural language processing, transfer learning, and real-world applications. The paper focuses on important research topics such as uncertainty estimation, handling of class imbalance, domain adaptation, fairness, and the creation of strong evaluation metrics and benchmarks. It also shows that learning methods inspired by humans and guided by questions can improve data efficiency and help models learn more effectively. In addition, this paper talks about current challenges in the field, including the need to rebuild trust, ensure reproducibility, and deal with inconsistent methodologies. It points out that AL often gives better results than passive learning, especially when good evaluation measures are used. This work aims to be useful for both researchers and practitioners by providing key insights and proposing directions for future progress in active learning.
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