A two-stage spiking meta-learning method for few-shot classification

Qiugang Zhan, Bingchao Wang, Anning Jiang, Xiurui Xie, Malu Zhang, Guisong Liu

Abstract: Highlights•We explore an effective two-stage SNN meta-learning framework.•A CKA-based non-parametric classifier is proposed for better capturing spiking temporal feature similarities.•The framework outperforms the existing SNN meta-learning methods and has lower energy consumption than ANN.