[2511.22344] Cleaning the Pool: Progressive Filtering of Unlabeled Pools in Deep Active Learning

Computer Science > Machine Learning arXiv:2511.22344 (cs) [Submitted on 27 Nov 2025 (v1), last revised 26 Mar 2026 (this version, v2)] Title:Cleaning the Pool: Progressive Filtering of Unlabeled Pools in Deep Active Learning Authors:Denis Huseljic, Marek Herde, Lukas Rauch, Paul Hahn, Bernhard Sick View a PDF of the paper titled Cleaning the Pool: Progressive Filtering of Unlabeled Pools in Deep Active Learning, by Denis Huseljic and 4 other authors View PDF HTML (experimental) Abstract:Existing active learning (AL) strategies capture fundamentally different notions of data value, e.g., uncertainty or representativeness. Consequently, the effectiveness of strategies can vary substantially across datasets, models, and even AL cycles. Committing to a single strategy risks suboptimal performance, as no single strategy dominates throughout the entire AL process. We introduce REFINE, an ensemble AL method that combines multiple strategies without knowing in advance which will perform best. In each AL cycle, REFINE operates in two stages: (1) Progressive filtering iteratively refines the unlabeled pool by considering an ensemble of AL strategies, retaining promising candidates capturing different notions of value. (2) Coverage-based selection then chooses a final batch from this refined pool, ensuring all previously identified notions of value are accounted for. Extensive experiments across 6 classification datasets and 3 foundation models show that REFINE consistently outperfor...