SAAL: Sharpness-Aware Active Learning

While deep neural networks play significant roles in many research areas, they are also prone to overfitting problems under limited data instances. To overcome overfitting, this paper introduces the first active learning method to incorporate the sharpness of loss space into the acquisition function. Specifically, our proposed method, Sharpness-Aware Active Learning (SAAL), constructs its acquisition function by selecting unlabeled instances whose perturbed loss becomes maximum. Unlike the Sharpness-Aware learning with fully-labeled datasets, we design a pseudolabeling mechanism to anticipate the perturbed loss w.r.t. the ground-truth label, which we provide the theoretical bound for the optimization. We conduct experiments on various benchmark datasets for vision-based tasks in image classification, object detection, and domain adaptive semantic segmentation. The experimental results confirm that SAAL outperforms the baselines by selecting instances that have the potentially maximal perturbation on the loss. The code is available at https://github.com/ YoonyeongKim/SAAL .