Rethinking Guidance

"Rethinking Guidance" in machine learning and robotics focuses on improving how models and agents utilize information to achieve desired outcomes, addressing limitations in existing methods. Current research explores diverse guidance strategies, including modifying score functions in diffusion models, leveraging various data types (text, images, masks) for few-shot learning, and employing novel label encoding techniques to better utilize unlabeled data. These advancements aim to enhance model performance, particularly in data-scarce scenarios, and improve the efficiency and robustness of autonomous systems, impacting fields ranging from image generation to robotic path planning.