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

TL;DR

This comprehensive survey reviews recent advances in deep active learning, covering methods, applications, challenges, and future directions to guide researchers in this rapidly evolving field.

Contribution

It provides an organized taxonomy of DAL methods, summarizes key datasets and baselines, and discusses applications and challenges, filling a gap in existing literature.

Findings

Systematic taxonomy of DAL methods across five perspectives.

Summary of main applications in NLP, CV, and DM.

Discussion of current challenges and future research directions.

Abstract

Active learning seeks to achieve strong performance with fewer training samples. It does this by iteratively asking an oracle to label new 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 learning-based 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 main applications of DAL in Natural Language Processing (NLP), Computer Vision (CV), and Data Mining (DM), etc. 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.