Low Data Regime

The "low data regime" in machine learning focuses on developing methods that achieve high performance with limited training data, a crucial challenge across many scientific domains. Current research emphasizes generative models, which synthesize additional data to augment scarce datasets, and techniques like transfer learning and self-supervised learning, which leverage pre-trained models or unlabeled data to improve performance on target tasks. These advancements are particularly impactful for applications where acquiring large labeled datasets is expensive, time-consuming, or ethically problematic, such as in medical imaging, drug discovery, and other specialized fields.