Synergy Between the Strong and the Weak: Spiking Neural Networks are Inherently Self-Distillers

Documenti analoghi

• Self-Distillation Learning Based on Temporal-Spatial Consistency for Spiking Neural Networks
  di: Zuo, Lin, et al.
  Pubblicazione: (2024)
• Rethinking Spiking Neural Networks from an Ensemble Learning Perspective
  di: Ding, Yongqi, et al.
  Pubblicazione: (2025)
• Toward End-to-End Bearing Fault Diagnosis for Industrial Scenarios with Spiking Neural Networks
  di: Zuo, Lin, et al.
  Pubblicazione: (2024)
• Shrinking Your TimeStep: Towards Low-Latency Neuromorphic Object Recognition with Spiking Neural Network
  di: Ding, Yongqi, et al.
  Pubblicazione: (2024)
• Temporal Reversal Regularization for Spiking Neural Networks: Hybrid Spatio-Temporal Invariance for Generalization
  di: Zuo, Lin, et al.
  Pubblicazione: (2024)