Although few-shot learning (FSL) has achieved great progress, it is still an enormous challenge especially when the source and target sets are from different domains, which is also known as cross-domain few-shot learning (CD-FSL). Utilizing more source domain data is an effective way to improve the performance of CD-FSL. However, knowledge from different source domains may entangle and confuse with each other, which hurts the performance on the target domain.
Team-knowledge distillation for multiple cross-domain, few-shot learning
