# Parting with Illusions about Deep Active Learning

arXiv: https://arxiv.org/abs/1912.05361 · HTML: https://mlsanity.com/paper/1912.05361/

## Key facts

- Cited by 38 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Dec 2019.

## Built on by (most cited first, 30 of 38)

- [Active Learning on a Budget: Opposite Strategies Suit High and Low Budgets](https://mlsanity.com/paper/2202.02794/) (2022)
- [Active Learning Through a Covering Lens](https://mlsanity.com/paper/2205.11320/) (2022)
- [Navigating the Pitfalls of Active Learning Evaluation: A Systematic Framework for Meaningful Performance Assessment](https://mlsanity.com/paper/2301.10625/) (2023)
- [Making Your First Choice: To Address Cold Start Problem in Vision Active Learning](https://mlsanity.com/paper/2210.02442/) (2022)
- [ValUES: A Framework for Systematic Validation of Uncertainty Estimation in Semantic Segmentation](https://mlsanity.com/paper/2401.08501/) (2024)
- [A Practitioner's Guide to Continual Multimodal Pretraining](https://mlsanity.com/paper/2408.14471/) (2024)
- [Active learning for medical image segmentation with stochastic batches](https://mlsanity.com/paper/2301.07670/) (2023)
- [Model Extraction Attacks Revisited](https://mlsanity.com/paper/2312.05386/) (2023)
- [LabelBench: A Comprehensive Framework for Benchmarking Adaptive Label-Efficient Learning](https://mlsanity.com/paper/2306.09910/) (2023)
- [Towards Good Practices for Efficiently Annotating Large-Scale Image Classification Datasets](https://mlsanity.com/paper/2104.12690/) (2021)
- [How to Select Which Active Learning Strategy is Best Suited for Your Specific Problem and Budget](https://mlsanity.com/paper/2306.03543/) (2023)
- [Uncertainty Estimation for Molecules: Desiderata and Methods](https://mlsanity.com/paper/2306.14916/) (2023)
- [Best Practices in Active Learning for Semantic Segmentation](https://mlsanity.com/paper/2302.04075/) (2023)
- [Making Better Use of Unlabelled Data in Bayesian Active Learning](https://mlsanity.com/paper/2404.17249/) (2024)
- [COLosSAL: A Benchmark for Cold-start Active Learning for 3D Medical Image Segmentation](https://mlsanity.com/paper/2307.12004/) (2023)
- [An Empirical Study on the Efficacy of Deep Active Learning for Image Classification](https://mlsanity.com/paper/2212.03088/) (2022)
- [Bridging Diversity and Uncertainty in Active learning with Self-Supervised Pre-Training](https://mlsanity.com/paper/2403.03728/) (2024)
- [A Review of Bayesian Uncertainty Quantification in Deep Probabilistic Image Segmentation](https://mlsanity.com/paper/2411.16370/) (2024)
- [Active Self-Semi-Supervised Learning for Few Labeled Samples](https://mlsanity.com/paper/2203.04560/) (2022)
- [Rethinking the Data Annotation Process for Multi-view 3D Pose Estimation with Active Learning and Self-Training](https://mlsanity.com/paper/2112.13709/) (2021)
- [NTKCPL: Active Learning on Top of Self-Supervised Model by Estimating True Coverage](https://mlsanity.com/paper/2306.04099/) (2023)
- [Streamlining the Development of Active Learning Methods in Real-World Object Detection](https://mlsanity.com/paper/2508.19906/) (2025)
- [Scalable Batch-Mode Deep Bayesian Active Learning via Equivalence Class Annealing](https://mlsanity.com/paper/2112.13737/) (2021)
- [Training Ensembles with Inliers and Outliers for Semi-supervised Active Learning](https://mlsanity.com/paper/2307.03741/) (2023)
- [How To Overcome Confirmation Bias in Semi-Supervised Image Classification By Active Learning](https://mlsanity.com/paper/2308.08224/) (2023)
- [Direct Acquisition Optimization for Low-Budget Active Learning](https://mlsanity.com/paper/2402.06045/) (2024)
- [SUPClust: Active Learning at the Boundaries](https://mlsanity.com/paper/2403.03741/) (2024)
- [IAdet: Simplest human-in-the-loop object detection](https://mlsanity.com/paper/2307.01582/) (2023)
- [Exploring UMAP in hybrid models of entropy-based and representativeness sampling for active learning in biomedical segmentation](https://mlsanity.com/paper/2312.10361/) (2023)
- [Finally Outshining the Random Baseline: A Simple and Effective Solution for Active Learning in 3D Biomedical Imaging](https://mlsanity.com/paper/2601.13677/) (2026)

## Abstract (arXiv)

Active learning aims to reduce the high labeling cost involved in training machine learning models on large datasets by efficiently labeling only the most informative samples. Recently, deep active learning has shown success on various tasks. However, the conventional evaluation scheme used for deep active learning is below par. Current methods disregard some apparent parallel work in the closely related fields. Active learning methods are quite sensitive w.r.t. changes in the training procedure like data augmentation. They improve by a large-margin when integrated with semi-supervised learning, but barely perform better than the random baseline. We re-implement various latest active learning approaches for image classification and evaluate them under more realistic settings. We further validate our findings for semantic segmentation. Based on our observations, we realistically assess the current state of the field and propose a more suitable evaluation protocol.