Recent Advancements in Active Learning

초록

Active learning (AL) aims to maximize model performance while minimizing annotation costs. With the rapid adoption of deep learning, AL approaches have evolved to meet contemporary demands. We systematically examine the literature published from 2018 to May 2025, focusing on four key trends: batch-mode selection, transfer learning integration, multi-strategy querying, and extension to diverse application domains. In addition, we summarize classical AL approaches. While observations show that combining AL with deep learning significantly enhances data efficiency, a critical limitation remains: the lack of standardized evaluation protocols across studies hinders precise comparisons. Nevertheless, we find that AL is well-aligned with modern trends, and we offer insights into underexplored opportunities to guide future research within the machine learning community. © 2026 by the authors.

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