Semi-Supervised Few-Shot Learning with Pseudo Label Refinement

Few-shot classification aims at recognising novel categories with very limited labelled samples. Although substantial achievements have been obtained, few-shot classification remains challenging due to the scarcity of labelled examples. Recent studies resort to leveraging unlabelled data to expand the training set using pseudo labelling, but this strategy often yields significant label noise. In this work, we introduce a new baseline method for semi-supervised few-shot learning by iterative pseudo label refinement to reduce noise. Then, we investigate the label noise propagation problem and improve the baseline with a denoising network to learn distributions of clean and noisy pseudo-labelled examples via a mixture model. This helps to estimate confidence values of pseudo labelled examples and to select the reliable ones with less noise for iteratively refining a few-shot classifier. Extensive experiments on three widely used benchmarks, minilma- genet, tieredImagenet and CIFAR-FS, show the superiority of the proposed methods over the state-of-the-art methods.