  Few-shot learning aims to learn to generalize a classifier to novel classes
with limited labeled data. Transductive inference that utilizes unlabeled test
set to deal with low-data problem has been employed for few-shot learning in
recent literature. Yet, these methods do not explicitly exploit the manifold
structures of semantic clusters, which is inefficient for transductive
inference. In this paper, we propose a novel Progressive Cluster Purification
(PCP) method for transductive few-shot learning. The PCP can progressively
purify the cluster by exploring the semantic interdependency in the individual
cluster space. Specifically, the PCP consists of two-level operations:
inter-class classification and intra-class transduction. The inter-class
classification partitions all the test samples into several clusters by
comparing the test samples with the prototypes. The intra-class transduction
effectively explores trustworthy test samples for each cluster by modeling data
relations within a cluster as well as among different clusters. Then, it
refines the prototypes to better represent the real distribution of semantic
clusters. The refined prototypes are used to remeasure all the test instances
and purify each cluster. Furthermore, the inter-class classification and the
intra-class transduction are extremely flexible to be repeated several times to
progressively purify the clusters. Experimental results are provided on two
datasets: miniImageNet dataset and tieredImageNet dataset. The comparison
results demonstrate the effectiveness of our approach and show that our
approach outperforms the state-of-the-art methods on both datasets.
