Few-Shot Learning with Representative Global Prototype

Yukun Liu, Daming Shi, Hexiu Lin

Abstract: Few-shot learning is often challenged by low generalization performance due to the assumption that the data distribution of novel classes and base classes is similar while the model is trained only on the base classes. To mitigate the above issues, we propose a few-shot learning with representative global prototype method. Specifically, to enhance the generalization to novel classes, we propose a method to jointly train the base classes and the novel classes, using selected representative and non-representative samples to optimize representative global prototypes, respectively. Additionally, a method that organically combines the sample of base classes conditional on semantic embedding to generate new samples of novel classes with the original data is proposed to enhance the data of novel classes. Results show that this training method improves the model's ability to describe novel classes, improving the classification performance for a few shots. Intensive experiments have been conducted on two popular benchmark datasets, and the experimental results show that this method significantly improves the classification ability of few-shot learning tasks and achieves state-of-the-art performance.

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