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Few-shot learning (FSL) is popular due to its ability to adapt to novelclasses. Compared with inductive few-shot learning, transductive modelstypically perform better as they leverage all samples of the query set. The twoexisting classes of methods, prototype-based and graph-based, have thedisadvantages of inaccurate prototype estimation and sub-optimal graphconstruction with kernel functions, respectively. In this paper, we propose anovel prototype-based label propagation to solve these issues. Specifically,our graph construction is based on the relation between prototypes and samplesrather than between samples. As prototypes are being updated, the graphchanges. We also estimate the label of each prototype instead of considering aprototype be the class centre. On mini-ImageNet, tiered-ImageNet, CIFAR-FS andCUB datasets, we show the proposed method outperforms other state-of-the-artmethods in transductive FSL and semi-supervised FSL when some unlabeled dataaccompanies the novel few-shot task.

Transductive Few-shot Learning with Prototype-based Label Propagation by Iterative Graph Refinement