Cross Domain Few Shot

Cross-Domain Few-Shot is a machine learning technique designed to achieve rapid adaptation and efficient learning across different domains with only a few samples. The core objective of this method is to enhance the model's generalization capability in new domains and reduce its reliance on large-scale labeled data. Its application value lies in effectively addressing the issue of data scarcity in the real world, accelerating the deployment and optimization of models in various scenarios.