Definition
Few-shot learning is an artificial intelligence technique where a machine learning model is trained to perform a task using only a very small number of labeled examples. Instead of requiring thousands or millions of data points, it leverages prior knowledge or meta-learning to quickly adapt to new categories or tasks. This approach allows AI systems to generalize effectively from limited data, making them more versatile and efficient. For instance, a few-shot model could learn to identify a new type of bird after seeing just a handful of images. This differs from traditional supervised learning, which typically demands extensive datasets for accurate performance. You will encounter few-shot learning concepts in advanced AI and machine learning certifications.