Limited Data

Limited data poses a significant challenge across numerous machine learning applications, hindering the development of accurate and robust models. Current research focuses on mitigating this limitation through techniques like data augmentation, transfer learning (often employing pre-trained models such as transformers and GANs), self-supervised learning, and the incorporation of domain knowledge or other forms of regularization. These advancements are crucial for fields like medical imaging, natural language processing, and robotics, where large, labeled datasets are often unavailable or prohibitively expensive to acquire, enabling progress in applications with limited data availability.