CKADisor: Central Kernel-Aligned Distillation for Efficient Event-Driven Object Recognition

Spiking neural networks (SNNs) are one of the best practices for efficient event-driven object recognition. To achieve high recognition accuracy, existing methods generally accumulate sufficient binary spike signals over long time steps. As a result, the required power consumption can be comparable even to the traditional artificial neural networks (ANNs). This paper introduces CKADisor, leveraging an ANN teacher to guide the direct training of an SNN student under a shorter time-step setting. To mitigate the representation structure mismatch between ANNs and SNNs, we develop a central-kernel aligned distillation strategy that measures layer-wise feature discrepancies and integrates them into the corresponding optimization objectives. Extensive experiments on three popular event datasets demonstrate that our CKADisor achieves higher accuracy than several state-of-the-art methods under a short time-step setting (T=5) using the same architecture.