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 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 $p\in (0, +\infty)$. Notably, in the case of $p=2$, our result substantially improves the bound of applying (Gajjar et al., 2023) to our setting. Experimental results on 8 benchmarks show that our one-shot approach achieves competitive performances with the state-of-the-art AL methods for multiple target models.
One-shot Active Learning Based on Lewis Weight Sampling for Multiple Deep Models
Sheng-Jun Huang Yi Li Yiming Sun Ying-Peng Tang