Few-Shot Learning

Few-shot learning is a type of machine learning where a model learns to perform a task using only a small number of examples. Unlike traditional models that require large datasets, few-shot learning enables systems to generalize quickly from limited data, similar to how humans can recognize new objects or concepts after seeing just a few instances. It leverages prior knowledge or advanced algorithms to make accurate predictions or classifications with minimal examples, making it efficient for applications where collecting extensive data is challenging or costly.