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[2510.25926] Active Learning with Task-Driven Representations for Messy Pools
Summary
This article presents a novel approach to active learning by introducing task-driven representations that adapt during the learning process, enhancing performance in messy data environments.
Why It Matters
Active learning is crucial for efficiently utilizing data, especially in messy pools where data relevance varies. This research addresses limitations in current methods by proposing adaptive representations, potentially improving machine learning outcomes across various applications.
Key Takeaways
- Current active learning methods often rely on fixed, unsupervised representations.
- Task-driven representations can significantly enhance performance in messy data environments.
- The study introduces two strategies for learning these representations, improving empirical results.
- Adaptive learning approaches can lead to better data utilization and task relevance.
- This research contributes to the ongoing development of more effective machine learning techniques.
Computer Science > Machine Learning arXiv:2510.25926 (cs) [Submitted on 29 Oct 2025 (v1), last revised 12 Feb 2026 (this version, v2)] Title:Active Learning with Task-Driven Representations for Messy Pools Authors:Kianoosh Ashouritaklimi, Tom Rainforth View a PDF of the paper titled Active Learning with Task-Driven Representations for Messy Pools, by Kianoosh Ashouritaklimi and 1 other authors View PDF HTML (experimental) Abstract:Active learning has the potential to be especially useful for messy, uncurated pools where datapoints vary in relevance to the target task. However, state-of-the-art approaches to this problem currently rely on using fixed, unsupervised representations of the pool, focusing on modifying the acquisition function instead. We show that this model setup can undermine their effectiveness at dealing with messy pools, as such representations can fail to capture important information relevant to the task. To address this, we propose using task-driven representations that are periodically updated during the active learning process using the previously collected labels. We introduce two specific strategies for learning these representations, one based on directly learning semi-supervised representations and the other based on supervised fine-tuning of an initial unsupervised representation. We find that both significantly improve empirical performance over using unsupervised or pretrained representations. Subjects: Machine Learning (cs.LG) Cite as: arXiv:2...
