首页|A Survey on Deep Active Learning: Recent Advances and New Frontiers

A Survey on Deep Active Learning: Recent Advances and New Frontiers

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  • NETL
  • NSTL
  • IEEE
Active learning seeks to achieve strong performance with fewer training samples. It does this by iteratively asking an oracle to label newly selected samples in a human-in-the-loop manner. This technique has gained increasing popularity due to its broad applicability, yet its survey papers, especially for deep active learning (DAL), remain scarce. Therefore, we conduct an advanced and comprehensive survey on DAL. We first introduce reviewed paper collection and filtering. Second, we formally define the DAL task and summarize the most influential baselines and widely used datasets. Third, we systematically provide a taxonomy of DAL methods from five perspectives, including annotation types, query strategies, deep model architectures, learning paradigms, and training processes, and objectively analyze their strengths and weaknesses. Then, we comprehensively summarize the main applications of DAL in natural language processing (NLP), computer vision (CV), data mining (DM), and so on. Finally, we discuss challenges and perspectives after a detailed analysis of current studies. This work aims to serve as a useful and quick guide for researchers in overcoming difficulties in DAL. We hope that this survey will spur further progress in this burgeoning field.

Task analysisTrainingSurveysAnnotationsTaxonomyElectronic mailDeep learning

Dongyuan Li、Zhen Wang、Yankai Chen、Renhe Jiang、Weiping Ding、Manabu Okumura

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School of Information and Communication Engineering, Institute of Innovative Research, Tokyo Institute of Technology, Tokyo, Japan

School of Computer Science and Engineering, The Chinese University of Hong Kong, shatin, Hong Kong

Center for Spatial Information Science, The University of Tokyo, Tokyo, Japan

School of Information Science and Technology, Nantong University, Nantong, China

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2025

10.1109/TNNLS.2024.3396463

IEEE transactions on neural networks and learning systems

IEEE transactions on neural networks and learning systems

ISSN：
年,卷(期)：2025.36(4)
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