# Similarity Search for Efficient Active Learning and Search of Rare Concepts

arXiv: https://arxiv.org/abs/2007.00077 · HTML: https://mlsanity.com/paper/2007.00077/

## Key facts

- Cited by 24 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Jun 2020.

## Built on by (most cited first, 24 of 24)

- [A Survey on Deep Active Learning: Recent Advances and New Frontiers](https://mlsanity.com/paper/2405.00334/) (2024)
- [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)
- [Small-Text: Active Learning for Text Classification in Python](https://mlsanity.com/paper/2107.10314/) (2021)
- [Algorithm Selection for Deep Active Learning with Imbalanced Datasets](https://mlsanity.com/paper/2302.07317/) (2023)
- [DeDrift: Robust Similarity Search under Content Drift](https://mlsanity.com/paper/2308.02752/) (2023)
- [Agile Modeling: From Concept to Classifier in Minutes](https://mlsanity.com/paper/2302.12948/) (2023)
- [Accelerating Batch Active Learning Using Continual Learning Techniques](https://mlsanity.com/paper/2305.06408/) (2023)
- [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)
- [From Categories to Classifiers: Name-Only Continual Learning by Exploring the Web](https://mlsanity.com/paper/2311.11293/) (2023)
- [AnchorAL: Computationally Efficient Active Learning for Large and Imbalanced Datasets](https://mlsanity.com/paper/2404.05623/) (2024)
- [Deep Active Learning in the Open World](https://mlsanity.com/paper/2411.06353/) (2024)
- [StreetSurfaceVis: a dataset of crowdsourced street-level imagery annotated by road surface type and quality](https://mlsanity.com/paper/2407.21454/) (2024)
- [A Cost-Effective LLM-based Approach to Identify Wildlife Trafficking in Online Marketplaces](https://mlsanity.com/paper/2504.21211/) (2025)
- [Active Few-Shot Fine-Tuning](https://mlsanity.com/paper/2402.15441/) (2024)
- [Amortized nonmyopic active search via deep imitation learning](https://mlsanity.com/paper/2405.15031/) (2024)
- [COBRA: COmBinatorial Retrieval Augmentation for Few-Shot Adaptation](https://mlsanity.com/paper/2412.17684/) (2024)
- [Improving traffic sign recognition by active search](https://mlsanity.com/paper/2111.14426/) (2021)
- [Semi-Automated Construction of Food Composition Knowledge Base](https://mlsanity.com/paper/2301.11322/) (2023)
- [Sparse Linear Bandits with Blocking Constraints](https://mlsanity.com/paper/2410.20041/) (2024)
- [Goal-Oriented Influence-Maximizing Data Acquisition for Learning and Optimization](https://mlsanity.com/paper/2602.19578/) (2026)
- [FunnelAL: Retrieve-then-Rank Active Learning for Single-Class Discovery](https://mlsanity.com/paper/2607.25276/) (2026)

## Abstract (arXiv)

Many active learning and search approaches are intractable for large-scale industrial settings with billions of unlabeled examples. Existing approaches search globally for the optimal examples to label, scaling linearly or even quadratically with the unlabeled data. In this paper, we improve the computational efficiency of active learning and search methods by restricting the candidate pool for labeling to the nearest neighbors of the currently labeled set instead of scanning over all of the unlabeled data. We evaluate several selection strategies in this setting on three large-scale computer vision datasets: ImageNet, OpenImages, and a de-identified and aggregated dataset of 10 billion images provided by a large internet company. Our approach achieved similar mean average precision and recall as the traditional global approach while reducing the computational cost of selection by up to three orders of magnitude, thus enabling web-scale active learning.