Novel Sample

Novel sample generation and utilization are key research areas impacting various machine learning applications. Current efforts focus on improving sample selection for contrastive learning and other deep learning models, including strategies to identify and leverage informative positive and negative samples, and mitigating the effects of adversarial or low-quality data. These advancements aim to enhance model generalization, particularly in scenarios with limited data, and improve the performance of tasks such as classification and novelty detection in diverse data types, including images and time series. The resulting improvements in data efficiency and model robustness have significant implications for fields ranging from materials science to healthcare, where data acquisition can be expensive or ethically challenging.