Few-shot learning is an AI technique that allows a model to learn a new task or recognize a new object after seeing a very small number of examples. This is a significant advancement over traditional machine learning, which can require thousands of examples to achieve mastery of a task.
Few-shot learning is especially valuable in specialized industries where there’s not a huge amount of training data. A few-shot learning model could quickly learn how to identify a defect in a hyper-specialized machine that there is limited training data on. This allows the model to quickly understand new challenges and help prevent them with minimal training time.
