few-shot

Machine learning approach that aims to train models to perform tasks with a very limited number of training examples. Few-shot learning includes techniques like meta-learning, transfer learning, data augmentation, and metric learning, making it valuable in scenarios where data is scarce or expensive to obtain. This approach is particularly beneficial for researchers and developers working in fields with limited labeled data, such as medical imaging or rare language processing.