Few-Shot Learning

Few-shot learning is a technique in machine learning in which models can learn effectively with just a few training examples. This is particularly useful in situations where large amounts of data are not available or difficult to obtain. Few-shot learning uses transfer learning and meta-learning to transfer knowledge from related tasks and quickly apply it to new problems. This method significantly improves the flexibility and adaptability of AI systems.