A contrastive model with local factor clustering for semi-supervised few-shot learning

Learning novel classes with a few samples per class is a very challenging task in deep learning. To mitigate this issue, previous studies have utilized an additional dataset with extensively labeled samples to realize transfer learning. Alternatively, many studies have used unlabeled samples that originated from the novel dataset to achieve few-shot learning, i.e., semi-supervised few-shot learning. In this paper, an easy but efficient semi-supervised few-shot learning model is proposed to address the embeddings mismatch problem that results from inconsistent data distributions between the novel and base datasets, where samples with the same label approach each other while samples with different labels separate from each other in the feature space. This model emphasizes pseudo-labeling guided contrastive learning. We also develop a novel local factor clustering module to improve the ability to obtain pseudo-labels from unlabeled samples, and this module fuses the local feature information of labeled and unlabeled samples. We report our experimental results on the mini-ImageNet and tiered-ImageNet datasets for both five-way one-shot and five-way five-shot settings and achieve better performance than previous models. In particular, the classification accuracy of our model is improved by approximately 11.53% and 14.87% compared to the most advanced semi-supervised few-shot learning model we know in the five-way one-shot scenario. Moreover, ablation experiments in this paper show that our proposed clustering strategy demonstrates accuracy improvements of about 4.00% in the five-way one-shot and five-way five-shot scenarios compared to two popular clustering methods.

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