ew-shot Learning

Few-shot learning is a type of machine learning where a model is trained to recognize new objects or concepts based on only a few examples. Traditional machine learning models require a large amount of data to be trained effectively, but few-shot learning aims to reduce this data requirement by leveraging prior knowledge or experience. Few-shot learning can be useful in scenarios where obtaining large amounts of labeled data is difficult or expensive, such as in medical diagnosis or natural language processing. Some popular few-shot learning techniques include siamese networks, meta-learning, and prototypical networks.