  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. The proposed method is simple and compatible with existing SNN
training pipelines. Using the proposed augmentation, for the first time, we
demonstrate the feasibility of unsupervised contrastive learning for SNNs. We
conduct comprehensive experiments on prevailing neuromorphic vision benchmarks
and show that NDA yields substantial improvements over previous
state-of-the-art results. For example, the NDA-based SNN achieves accuracy gain
on CIFAR10-DVS and N-Caltech 101 by 10.1% and 13.7%, respectively. Code is
available on GitHub https://github.com/Intelligent-Computing-Lab-Yale/NDA_SNN
