Title: SAMOSA: Sharpness Aware Minimization for Open Set Active learning
Primary area: other topics in machine learning (i.e., none of the above)
Keywords: active learning, sam, sharpness aware minimization, data typicality

Abstract: Modern machine learning solutions require extensive data collection where labeling remains costly. To reduce this burden, open set active learning approaches aim to select informative samples from a large pool of unlabeled dataset that includes irrelevant or unknown classes. In this context, we propose Sharpness Aware Minimization for Open Set Active Learning (SAMOSA) as an effective querying algorithm. Building on theoretical findings concerning the impact that data typicality has on generalization properties of traditional SGD and sharpness-aware minimization, SAMOSA actively queries samples based on their typicality. SAMOSA effectively identifies atypical samples that belong to regions of the embedding manifold clustered close to the model decision boundaries. Therefore, SAMOSA prioritizes the samples which are both highly informative for the targeted classes, and useful for distinguishing between targeted and unwanted classes.
Extensive experiments show that SAMOSA achieves up to 3\% accuracy improvement over the state-of-the-art across several datasets. The source code of our experiments is available at \url{https://anonymous.4open.science/r/samosa-EF8C}.

TL;DR: Leveraging discrepancy between SAM and SGD model proves effective for active learning