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cs.LG2025
Synergy Between the Strong and the Weak: Spiking Neural Networks are Inherently Self-Distillers
Yongqi Ding, Lin Zuo, Mengmeng Jing +3
Brain-inspired spiking neural networks (SNNs) promise to be a low-power alternative to computationally intensive artificial neural networks (ANNs), although performance gaps persis…
cs.LG2025★ 1 cited
Rethinking Spiking Neural Networks from an Ensemble Learning Perspective
Yongqi Ding, Lin Zuo, Mengmeng Jing +2
Spiking neural networks (SNNs) exhibit superior energy efficiency but suffer from limited performance. In this paper, we consider SNNs as ensembles of temporal subnetworks that sha…
cs.LG2024★ 1 cited
Self-Distillation Learning Based on Temporal-Spatial Consistency for Spiking Neural Networks
Lin Zuo, Yongqi Ding, Mengmeng Jing +2
Spiking neural networks (SNNs) have attracted considerable attention for their event-driven, low-power characteristics and high biological interpretability. Inspired by knowledge d…