selective-alignment

Proposes Selective Alignment Knowledge Distillation (SeAl-KD) for Spiking Neural Networks, which selectively aligns class-level and temporal knowledge by equalizing competing logits at erroneous timesteps and reweighting temporal alignment based on confidence and inter-timestep similarity, achieving consistent improvements over existing distillation methods on static and neuromorphic datasets.