Outlier-Aware Contrastive Learning

Contrastive learning (a machine learning technique where the AI learns to group similar items together and push different items apart) can suffer from sampling bias when similar samples belong to different classes or dissimilar samples belong to the same class, hurting classification accuracy. This paper proposes using out-of-distribution (OOD) detection, which identifies and masks unusual or misclassified samples, to create a better contrastive learning model that can work without needing a separate collection of known unusual samples. The authors generate synthetic samples at the boundary between normal and unusual data to train an improved detector that produces more reliable classifications.