Neuromorphic Data Augmentation for Training Spiking Neural Networks

Understand

Developing neuromorphic intelligence on event-based datasets with Spiking Neural Networks (SNNs) has recently attracted much research attention.

• However, the limited size of event-based datasets makes SNNs prone to overfitting and unstable convergence.
• This issue remains unexplored by previous academic works.
• In an effort to minimize this generalization gap, we propose Neuromorphic Data Augmentation (NDA), a family of geometric augmentations specifically designed for event-based datasets with the goal of significantly stabilizing the SNN training and reducing the generalization gap between training and test performance.