Open-set learning with augmented categories by exploiting unlabelled data
This addresses the challenge of handling novel categories in partially labelled datasets for machine learning applications, representing a novel integration rather than an incremental improvement.
The paper tackles the problem of open-set learning where novel categories can be present in both training and testing data, introducing a new policy called Open-LACU that unifies positive and unlabelled learning, semi-supervised learning, and open-set recognition, achieving state-of-the-art results.
Novel categories are commonly defined as those unobserved during training but present during testing. However, partially labelled training datasets can contain unlabelled training samples that belong to novel categories, meaning these can be present in training and testing. This research is the first to generalise between what we call observed-novel and unobserved-novel categories within a new learning policy called open-set learning with augmented category by exploiting unlabelled data or Open-LACU. After surveying existing learning policies, we introduce Open-LACU as a unified policy of positive and unlabelled learning, semi-supervised learning and open-set recognition. Subsequently, we develop the first Open-LACU model using an algorithmic training process of the relevant research fields. The proposed Open-LACU classifier achieves state-of-the-art and first-of-its-kind results.