# Active Learning at the ImageNet Scale

arXiv: https://arxiv.org/abs/2111.12880 · HTML: https://mlsanity.com/paper/2111.12880/

## Key facts

- Cited by 28 papers in the map (data through 14 Oct 2026).
- Builds on 25 of its 41 references that are in the map.
- First posted on arXiv 25 Nov 2021.

## Builds on (most cited first, 25 of 25)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [A Simple Framework for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2002.05709/) (2020)
- [ImageNet Large Scale Visual Recognition Challenge](https://mlsanity.com/paper/1409.0575/) (2014)
- [Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning](https://mlsanity.com/paper/1506.02142/) (2015)
- [Zero-Shot Text-to-Image Generation](https://mlsanity.com/paper/2102.12092/) (2021)
- [On the Opportunities and Risks of Foundation Models](https://mlsanity.com/paper/2108.07258/) (2021)
- [Improved Baselines with Momentum Contrastive Learning](https://mlsanity.com/paper/2003.04297/) (2020)
- [FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidence](https://mlsanity.com/paper/2001.07685/) (2020)
- [Learning Imbalanced Datasets with Label-Distribution-Aware Margin Loss](https://mlsanity.com/paper/1906.07413/) (2019)
- [Deep Bayesian Active Learning with Image Data](https://mlsanity.com/paper/1703.02910/) (2017)
- [Deep Batch Active Learning by Diverse, Uncertain Gradient Lower Bounds](https://mlsanity.com/paper/1906.03671/) (2019)
- [Wasserstein Auto-Encoders](https://mlsanity.com/paper/1711.01558/) (2017)
- [Are we done with ImageNet?](https://mlsanity.com/paper/2006.07159/) (2020)
- [Variational Adversarial Active Learning](https://mlsanity.com/paper/1904.00370/) (2019)
- [Efficient Self-supervised Vision Transformers for Representation Learning](https://mlsanity.com/paper/2106.09785/) (2021)
- [Batch Active Learning at Scale](https://mlsanity.com/paper/2107.14263/) (2021)
- [From ImageNet to Image Classification: Contextualizing Progress on Benchmarks](https://mlsanity.com/paper/2005.11295/) (2020)
- [Understanding Generalization through Visualizations](https://mlsanity.com/paper/1906.03291/) (2019)
- [Rethinking deep active learning: Using unlabeled data at model training](https://mlsanity.com/paper/1911.08177/) (2019)
- [Effective Evaluation of Deep Active Learning on Image Classification Tasks](https://mlsanity.com/paper/2106.15324/) (2021)
- [On the Marginal Benefit of Active Learning: Does Self-Supervision Eat Its Cake?](https://mlsanity.com/paper/2011.08121/) (2020)
- [Towards Good Practices for Efficiently Annotating Large-Scale Image Classification Datasets](https://mlsanity.com/paper/2104.12690/) (2021)

## Built on by (most cited first, 28 of 28)

- [Beyond neural scaling laws: beating power law scaling via data pruning](https://mlsanity.com/paper/2206.14486/) (2022)
- [Plex: Towards Reliability using Pretrained Large Model Extensions](https://mlsanity.com/paper/2207.07411/) (2022)
- [Anchor Points: Benchmarking Models with Much Fewer Examples](https://mlsanity.com/paper/2309.08638/) (2023)
- [Data Pruning via Moving-one-Sample-out](https://mlsanity.com/paper/2310.14664/) (2023)
- [Continuous Learning for Android Malware Detection](https://mlsanity.com/paper/2302.04332/) (2023)
- [GALAXY: Graph-based Active Learning at the Extreme](https://mlsanity.com/paper/2202.01402/) (2022)
- [LabelBench: A Comprehensive Framework for Benchmarking Adaptive Label-Efficient Learning](https://mlsanity.com/paper/2306.09910/) (2023)
- [Algorithm Selection for Deep Active Learning with Imbalanced Datasets](https://mlsanity.com/paper/2302.07317/) (2023)
- [Active Learning with Neural Networks: Insights from Nonparametric Statistics](https://mlsanity.com/paper/2210.08367/) (2022)
- [Learning Large-scale Neural Fields via Context Pruned Meta-Learning](https://mlsanity.com/paper/2302.00617/) (2023)
- [What Variables Affect Out-of-Distribution Generalization in Pretrained Models?](https://mlsanity.com/paper/2405.15018/) (2024)
- [Is margin all you need? An extensive empirical study of active learning on tabular data](https://mlsanity.com/paper/2210.03822/) (2022)
- [Making Better Use of Unlabelled Data in Bayesian Active Learning](https://mlsanity.com/paper/2404.17249/) (2024)
- [Improved Algorithm for Deep Active Learning under Imbalance via Optimal Separation](https://mlsanity.com/paper/2312.09196/) (2023)
- [AHA: Human-Assisted Out-of-Distribution Generalization and Detection](https://mlsanity.com/paper/2410.08000/) (2024)
- [Out-of-Distribution Learning with Human Feedback](https://mlsanity.com/paper/2408.07772/) (2024)
- [Deep Active Learning in the Open World](https://mlsanity.com/paper/2411.06353/) (2024)
- [Active Learning via Classifier Impact and Greedy Selection for Interactive Image Retrieval](https://mlsanity.com/paper/2412.02310/) (2024)
- [Feature Alignment: Rethinking Efficient Active Learning via Proxy in the Context of Pre-trained Models](https://mlsanity.com/paper/2403.01101/) (2024)
- [NTKCPL: Active Learning on Top of Self-Supervised Model by Estimating True Coverage](https://mlsanity.com/paper/2306.04099/) (2023)
- [Training Ensembles with Inliers and Outliers for Semi-supervised Active Learning](https://mlsanity.com/paper/2307.03741/) (2023)
- [Active Prompt Learning with Vision-Language Model Priors](https://mlsanity.com/paper/2411.16722/) (2024)
- [SHIFT3D: Synthesizing Hard Inputs For Tricking 3D Detectors](https://mlsanity.com/paper/2309.05810/) (2023)
- [Embracing Unknown Step by Step: Towards Reliable Sparse Training in Real World](https://mlsanity.com/paper/2403.20047/) (2024)
- [Active Transfer Prototypical Network: An Efficient Labeling Algorithm for Time-Series Data](https://mlsanity.com/paper/2209.14199/) (2022)
- [Revisit Micro-batch Clipping: Adaptive Data Pruning via Gradient Manipulation](https://mlsanity.com/paper/2408.16204/) (2024)
- [PCoreSet: Effective Active Learning through Knowledge Distillation from Vision-Language Models](https://mlsanity.com/paper/2506.00910/) (2025)
- [Active Learning with Foundation Model Priors: Efficient Learning under Class Imbalance](https://mlsanity.com/paper/2606.07630/) (2026)

## Abstract (arXiv)

Active learning (AL) algorithms aim to identify an optimal subset of data for annotation, such that deep neural networks (DNN) can achieve better performance when trained on this labeled subset. AL is especially impactful in industrial scale settings where data labeling costs are high and practitioners use every tool at their disposal to improve model performance. The recent success of self-supervised pretraining (SSP) highlights the importance of harnessing abundant unlabeled data to boost model performance. By combining AL with SSP, we can make use of unlabeled data while simultaneously labeling and training on particularly informative samples. In this work, we study a combination of AL and SSP on ImageNet. We find that performance on small toy datasets -- the typical benchmark setting in the literature -- is not representative of performance on ImageNet due to the class imbalanced samples selected by an active learner. Among the existing baselines we test, popular AL algorithms across a variety of small and large scale settings fail to outperform random sampling. To remedy the class-imbalance problem, we propose Balanced Selection (BASE), a simple, scalable AL algorithm that outperforms random sampling consistently by selecting more balanced samples for annotation than existing methods. Our code is available at: https://github.com/zeyademam/active_learning .