Understand
In this paper, we present a new method, Transductive Multi-Head Few-Shot learning (TMHFS), to address the Cross-Domain Few-Shot Learning (CD-FSL) challenge.
- The TMHFS method extends the Meta-Confidence Transduction (MCT) and Dense Feature-Matching Networks (DFMN) method [2] by introducing a new prediction head, i.e, an instance-wise global classification network based on semantic information, after the common feature embedding network.
- We train the embedding network with the multiple heads, i.e,, the MCT loss, the DFMN loss and the semantic classifier loss, simultaneously in the source domain.
- For the few-shot learning in the target domain, we first perform fine-tuning on the embedding network with only the semantic global classifier and the support instances, and then use the MCT part to predict labels of the query set with the fine-tuned embedding network.