Not All Timesteps Matter Equally: Selective Alignment Knowledge Distillation for Spiking Neural Networks

arXiv:2605.14252v2 Announce Type: replace Abstract: Spiking neural networks (SNNs), which are brain-inspired and spike-driven, achieve high energy efficiency. However, a performance gap between SNNs and artificial neural networks (ANNs) still remains. Knowledge distillation (KD) is commonly adopted to improve SNN performance, but existing methods typically enforce uniform alignment across all timesteps, either from a teacher network or through inter-temporal self-distillation, implicitly assuming that per-timestep predictions should be treated equally. In practice, SNN predictions vary and evo

Source: arXiv cs.LG — read the full report at the original publisher.

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