Unified machine learning: Open-set learning with augmented category by exploiting unlabelled data (Open-LACU)

Unifying semi-supervised learning (SSL) and open-set recognition into a single learning policy would facilitate the development of cost-efficient and application-grade classifiers. However, previous attempts do not clarify the difference between unobserved novel categories (those only seen during testing) and observed novel categories (those present in unlabelled training data). This study introduces Open-Set Learning with Augmented Category by Exploiting Unlabelled Data (Open-LACU), the first policy that generalises between both novel category types. We adapt the state-of-the-art OSR method of Margin Generative Adversarial Networks (Margin-GANs) into several Open-LACU configurations, setting the benchmarks for Open-LACU and offering unique insights into novelty detection using Margin-GANs. Finally, we highlight the significance of the Open-LACU policy by discussing the applications of semantic segmentation in remote sensing, object detection in radiology and disease identification through cough analysis. These applications include observed and unobserved novel categories, making Open-LACU essential for training classifiers in these big data domains.