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

arXiv: https://arxiv.org/abs/2405.00334 · HTML: https://mlsanity.com/paper/2405.00334/

## Key facts

- Cited by 54 papers in the map (data through 14 Oct 2026).
- Builds on 126 of its 213 references that are in the map.
- First posted on arXiv 1 May 2024.

## Builds on (most cited first, 30 of 126)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [The Cityscapes Dataset for Semantic Urban Scene Understanding](https://mlsanity.com/paper/1604.01685/) (2016)
- [Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles](https://mlsanity.com/paper/1612.01474/) (2016)
- [A Comprehensive Survey on Graph Neural Networks](https://mlsanity.com/paper/1901.00596/) (2019)
- [Character-level Convolutional Networks for Text Classification](https://mlsanity.com/paper/1509.01626/) (2015)
- [A large annotated corpus for learning natural language inference](https://mlsanity.com/paper/1508.05326/) (2015)
- [Deep Bayesian Active Learning with Image Data](https://mlsanity.com/paper/1703.02910/) (2017)
- [A Survey of Deep Active Learning](https://mlsanity.com/paper/2009.00236/) (2020)
- [BatchBALD: Efficient and Diverse Batch Acquisition for Deep Bayesian Active Learning](https://mlsanity.com/paper/1906.08158/) (2019)
- [Learning Loss for Active Learning](https://mlsanity.com/paper/1905.03677/) (2019)
- [YouTube-8M: A Large-Scale Video Classification Benchmark](https://mlsanity.com/paper/1609.08675/) (2016)
- [What Are Bayesian Neural Network Posteriors Really Like?](https://mlsanity.com/paper/2104.14421/) (2021)
- [Variational Adversarial Active Learning](https://mlsanity.com/paper/1904.00370/) (2019)
- [On the Stability of Fine-tuning BERT: Misconceptions, Explanations, and Strong Baselines](https://mlsanity.com/paper/2006.04884/) (2020)
- [Cost-Effective Active Learning for Deep Image Classification](https://mlsanity.com/paper/1701.03551/) (2017)
- [Bayesian Convolutional Neural Networks with Bernoulli Approximate Variational Inference](https://mlsanity.com/paper/1506.02158/) (2015)
- [Adversarial Active Learning for Deep Networks: a Margin Based Approach](https://mlsanity.com/paper/1802.09841/) (2018)
- [A Survey on Active Learning and Human-in-the-Loop Deep Learning for Medical Image Analysis](https://mlsanity.com/paper/1910.02923/) (2019)
- [Active Learning on a Budget: Opposite Strategies Suit High and Low Budgets](https://mlsanity.com/paper/2202.02794/) (2022)
- [Dataset Pruning: Reducing Training Data by Examining Generalization Influence](https://mlsanity.com/paper/2205.09329/) (2022)
- [Cold-start Active Learning through Self-supervised Language Modeling](https://mlsanity.com/paper/2010.09535/) (2020)
- [Batch Active Learning at Scale](https://mlsanity.com/paper/2107.14263/) (2021)
- [Consistency-based Semi-supervised Active Learning: Towards Minimizing Labeling Cost](https://mlsanity.com/paper/1910.07153/) (2019)
- [Learning Active Learning from Data](https://mlsanity.com/paper/1703.03365/) (2017)
- [Fast active learning for pure exploration in reinforcement learning](https://mlsanity.com/paper/2007.13442/) (2020)
- [A Comparative Survey of Deep Active Learning](https://mlsanity.com/paper/2203.13450/) (2022)

## Built on by (most cited first, 30 of 54)

- [Deep Learning based Visually Rich Document Content Understanding: A Survey](https://mlsanity.com/paper/2408.01287/) (2024)
- [Filter Images First, Generate Instructions Later: Pre-Instruction Data Selection for Visual Instruction Tuning](https://mlsanity.com/paper/2503.07591/) (2025)
- [Consensus-Driven Active Model Selection](https://mlsanity.com/paper/2507.23771/) (2025)
- [MultiBreak: A Scalable and Diverse Multi-turn Jailbreak Benchmark for Evaluating LLM Safety](https://mlsanity.com/paper/2605.01687/) (2026)
- [Active Test-time Vision-Language Navigation](https://mlsanity.com/paper/2506.06630/) (2025)
- [Out-of-Distribution Learning with Human Feedback](https://mlsanity.com/paper/2408.07772/) (2024)
- [Cohort-Based Active Modality Acquisition](https://mlsanity.com/paper/2505.16791/) (2025)
- [Quantifying and Narrowing the Unknown: Interactive Text-to-Video Retrieval via Uncertainty Minimization](https://mlsanity.com/paper/2507.15504/) (2025)
- [DriftGuard: Safety-Aware Multi-Monitor Detection and Selective Adaptation for Evolving Toxicity Moderation](https://mlsanity.com/paper/2606.28725/) (2026)
- [VickreyFeedback: Cost-efficient Data Construction for Reinforcement Learning from Human Feedback](https://mlsanity.com/paper/2409.18417/) (2024)
- [LPLgrad: Optimizing Active Learning Through Gradient Norm Sample Selection and Auxiliary Model Training](https://mlsanity.com/paper/2411.15217/) (2024)
- [Enhancing RAG with Active Learning on Conversation Records: Reject Incapables and Answer Capables](https://mlsanity.com/paper/2502.09073/) (2025)
- [PICore: Physics-Informed Unsupervised Coreset Selection for Data Efficient Neural Operator Training](https://mlsanity.com/paper/2507.17151/) (2025)
- [Prompts to Proxies: Emulating Human Preferences via a Compact LLM Ensemble](https://mlsanity.com/paper/2509.11311/) (2025)
- [IDEAL-M3D: Instance Diversity-Enriched Active Learning for Monocular 3D Detection](https://mlsanity.com/paper/2511.19301/) (2025)
- [Positive-First Most Ambiguous: A Simple Active Learning Criterion for Interactive Retrieval of Rare Categories](https://mlsanity.com/paper/2603.24480/) (2026)
- [Loss-Driven Bayesian Active Learning](https://mlsanity.com/paper/2604.11995/) (2026)
- [Active-SAOOD: Active Sparsely Annotated Oriented Object Detection in Remote Sensing Images](https://mlsanity.com/paper/2605.10162/) (2026)
- [A model-agnostic active learning approach for animal detection from camera traps](https://mlsanity.com/paper/2507.06537/) (2025)
- [SoK: Data Minimization in Machine Learning](https://mlsanity.com/paper/2508.10836/) (2025)
- [SSeg: Active Sparse Point-Label Augmentation for Semantic Segmentation](https://mlsanity.com/paper/2510.10163/) (2025)
- [Reducing Oracle Feedback with Vision-Language Embeddings for Preference-Based RL](https://mlsanity.com/paper/2603.28053/) (2026)
- [UniAlign: A Model-Agnostic Framework for Robust Network Traffic Classification under Distribution Shifts](https://mlsanity.com/paper/2605.17575/) (2026)
- [AvAtar: Learning to Align via Active Optimal Transport](https://mlsanity.com/paper/2605.24395/) (2026)
- [Sparse Linear Bandits with Blocking Constraints](https://mlsanity.com/paper/2410.20041/) (2024)
- [Federated Learning with Workload Reduction through Partial Training of Client Models and Entropy-Based Data Selection](https://mlsanity.com/paper/2501.00170/) (2024)
- [ALPET: Active Few-shot Learning for Citation Worthiness Detection in Low-Resource Wikipedia Languages](https://mlsanity.com/paper/2502.03292/) (2025)
- [Hierarchical graph sampling based minibatch learning with chain preservation and variance reduction](https://mlsanity.com/paper/2503.00860/) (2025)
- [Active Learning Methods for Efficient Data Utilization and Model Performance Enhancement](https://mlsanity.com/paper/2504.16136/) (2025)
- [PCoreSet: Effective Active Learning through Knowledge Distillation from Vision-Language Models](https://mlsanity.com/paper/2506.00910/) (2025)

## Abstract (arXiv)

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.