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.

few-shot was discovered on May 31st 2020 and it currently has a search volume of 3.6K with a growth of +271%.