One-shot Active Learning Based on Lewis Weight Sampling for Multiple Deep Models
This addresses the problem of reducing labeling costs for training multiple deep models, though it appears incremental as it builds on existing active learning frameworks.
The paper tackles the computational expense of iterative active learning for multiple deep models by proposing a one-shot method that queries all labels without repeated training, achieving competitive performance with state-of-the-art methods on 11 benchmarks.
Active learning (AL) for multiple target models aims to reduce labeled data querying while effectively training multiple models concurrently. Existing AL algorithms often rely on iterative model training, which can be computationally expensive, particularly for deep models. In this paper, we propose a one-shot AL method to address this challenge, which performs all label queries without repeated model training. Specifically, we extract different representations of the same dataset using distinct network backbones, and actively learn the linear prediction layer on each representation via an $\ell_p$-regression formulation. The regression problems are solved approximately by sampling and reweighting the unlabeled instances based on their maximum Lewis weights across the representations. An upper bound on the number of samples needed is provided with a rigorous analysis for $p\in [1, +\infty)$. Experimental results on 11 benchmarks show that our one-shot approach achieves competitive performances with the state-of-the-art AL methods for multiple target models.