Principal characteristic networks for few-shot learning

Abstract Few-shot learning aims to build a classifier that recognizes unseen new classes given only a few samples of them. Previous studies like prototypical networks utilized the mean of embedded support vectors to represent the prototype that is the representation of class and yield satisfactory results. However, the importance of these different embedded support vectors is not studied yet, which are valuable factors that could be used to push the limit of the few-shot learning. We propose a principal characteristic network that exploits the principal characteristic to better express prototype, computed by distributing weights based on embedded vectors’ different importance. The high-level abstract embedded vectors are extracted from our eResNet embedding network. In addition, we proposed a mixture loss function, which enlarges the inter-class distance in the embedding space for accurate classification. Extensive experimental results demonstrate that our network achieves state-of-the-art results on the Omniglot, miniImageNet and Cifar100 datasets.