Shot Support

Shot support, in the context of machine learning, focuses on improving model performance with limited training data, particularly in adapting to new domains or tasks. Current research emphasizes developing efficient algorithms and model architectures, such as those incorporating prototype memory banks or leveraging the capabilities of large language models (LLMs) for few-shot learning, often integrating them with test-time adaptation strategies. This area is significant because it addresses the limitations of data-hungry deep learning models, enabling more efficient and robust applications across diverse domains, from robotics and natural language processing to biomedical image analysis.