Single Example

Research on "single example" learning explores how to effectively train and utilize machine learning models with minimal training data, focusing on leveraging a single instance to generalize to unseen examples. Current efforts concentrate on adapting existing architectures like LLMs and diffusion models, employing techniques such as prompt engineering, contrastive learning, and data augmentation strategies to improve performance. This area is crucial for addressing data scarcity challenges across various domains, from computer vision and natural language processing to robotics and healthcare, enabling efficient model development and deployment in data-limited scenarios.