Few-shot learning via relation network based on coarse-grained granulation

Few-shot learning, which aims to identify new classes with very few samples, is an increasingly popular and crucial research topic in the machine learning. Many models use distance measurement to determine similarities among single samples and achieve accurate classification results. However, distance calculations incur substantial costs and time based on a single sample, and the linear measurement model cannot accurately represent the differences and connections between samples. This paper proposes a coarse-grained granulation relation network (CGRN) model for few-shot classification. First, all the single samples of each class are clustered into coarse grain to represent the feature information of all the class samples, which can significantly reduce computational complexities. Second, a relation network is built to measure the degree of similarity among the test samples and the coarse grain obtained above, which can reveal the differences and connections between the samples. The experimental results demonstrate that this model outperforms some popular distance measurement-based few-shot learning models. For example, CGRN is at least 0.5% better than other models in 20-way 5-shot on the Omniglot dataset and achieves 0.8% improvement over the second-best model in 5-way 1-shot on the tiered-ImageNet dataset.