aiqbench analysis
This paper combines active learning with knowledge distillation, using large vision-language models as zero- and few-shot teachers to guide which unlabeled samples get annotated. Its main claim is that selecting samples for diversity in the teacher's probability space, rather than in feature space, gives the most reliable gains under tight labeling budgets. For ML practitioners, the useful takeaway is that many standard active learning selectors underperform random sampling once distillation is applied, so selector choice should be benchmarked in the distillation setting rather than assumed to transfer.
PCoreSet: Effective Active Learning through Knowledge Distillation from Vision-Language Models
arXiv:2506.00910v3 Announce Type: replace Abstract: Knowledge distillation (KD) is a widely used framework for training compact, task-specific models by transferring the knowledge from teacher models. However, its application to active learning (AL), which aims to minimize annotation costs through iterative sample selection, remains underexplored. This gap stems from the fact that KD typically assumes access to sufficient labeled data, whereas AL operates in data-scarce scenarios where task-specific teacher models are often unavailable. In this paper, we first introduce ActiveKD, a framework that integrates AL with KD by leveraging the zero- and few-shot capabilities of large vision-language models (VLMs). A key aspect of ActiveKD is the structured prediction bias of VLMs--i.e., their predictions form clusters in the probability space. We regard this structure as an inductive bias of the teacher model, capturing generalizable output patterns beneficial to student learning. To exploit this bias, we propose Probabilistic CoreSet (PCoreSet), a selection strategy that maximizes coverage in the probability space rather than the feature space. PCoreSet strategically selects probabilistically diverse unlabeled samples, facilitating more efficient transfer of teacher knowledge under limited annotation budgets. Extensive evaluations on 11 datasets show that ActiveKD consistently improves every selection method over the no-distillation baseline (e.g., +29.07% on ImageNet, averaged over methods). Under ActiveKD, several widely-used AL selectors underperform Random and none consistently beats it; in contrast, PCoreSet is the only selector that consistently outperforms Random across 5 students and 3 teachers, always achieving the best performance except for the first 2 AL rounds. Our code is available at https://github.com/erjui/PCoreSet.
Abstract:Knowledge distillation (KD) is a widely used framework for training compact, task-specific models by transferring the knowledge from teacher models. However, its application to active learning (AL), which aims to minimize annotation costs through iterative sample selection, remains underexplored. This gap stems from the fact that KD typically assumes access to sufficient labeled data, whereas AL operates in data-scarce scenarios where task-specific teacher models are often unavailable. In this paper, we first introduce ActiveKD, a framework that integrates AL with KD by leveraging the zero- and few-shot capabilities of large vision-language models (VLMs). A key aspect of ActiveKD is the structured prediction bias of VLMs--i.e., their predictions form clusters in the probability space. We regard this structure as an inductive bias of the teacher model, capturing generalizable output patterns beneficial to student learning. To exploit this bias, we propose Probabilistic CoreSet (PCoreSet), a selection strategy that maximizes coverage in the probability space rather than the feature space. PCoreSet strategically selects probabilistically diverse unlabeled samples, facilitating more efficient transfer of teacher knowledge under limited annotation budgets. Extensive evaluations on 11 datasets show that ActiveKD consistently improves every selection method over the no-distillation baseline (e.g., +29.07% on ImageNet, averaged over methods). Under ActiveKD, several widely-used AL selectors underperform Random and none consistently beats it; in contrast, PCoreSet is the only selector that consistently outperforms Random across 5 students and 3 teachers, always achieving the best performance except for the first 2 AL rounds. Our code is available at this https URL.
46 pages, 24 figures, preprint. Updated manuscript and author affiliations; clarified ActiveKD/PCoreSet comparisons and the ClassBalanced baseline; expanded analysis and implementation details
Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
arXiv:2506.00910 [cs.LG]
(or arXiv:2506.00910v3 [cs.LG] for this version)
Full text under the publisher's open licence · 285 words