Shot Adaptation

Shot adaptation in machine learning focuses on efficiently adapting pre-trained models to new tasks or domains using only a limited number of examples (few-shot) or even a single example (one-shot). Current research emphasizes techniques like prompt engineering, adapter modules, and generative models (including diffusion models and GANs) to achieve this adaptation, often within the context of vision-language models and reinforcement learning. This field is significant because it addresses the limitations of traditional deep learning approaches that require massive datasets for effective generalization, paving the way for more robust and data-efficient AI systems across various applications, including robotics and medical imaging.