sMoBYAL: supervised contrastive active learning for image classification

Abstract

We propose a novel active learning framework for image classification -sMoBYAL. Our contribution is modifying MoBY -one of the highly effective self-supervised learning algorithms to utilize both labeled and unlabeled data for the active learning pipeline. Finally, we thoroughly evaluate and analyze the robustness and performance of our pipeline in image classification tasks. Our approach attains comparative outcomes, surpassing recent AL methods in terms of results.