Cross Domain Few Shot Learning

Cross-domain few-shot learning is an important branch of transfer learning, aiming to migrate models trained on the source domain to the target domain to address challenges such as unseen categories, inconsistent data distributions, and limited labeled data per class in the target domain. This task enhances the model's generalization and adaptability in new environments by effectively leveraging knowledge from the source domain, making it highly valuable for practical applications.