Neuromorphic Data Augmentation for Training Spiking Neural Networks

• URL: http://arxiv.org/abs/2203.06145v1
• Date: Fri, 11 Mar 2022 18:17:19 GMT
• Title: Neuromorphic Data Augmentation for Training Spiking Neural Networks
• Authors: Yuhang Li, Youngeun Kim, Hyoungseob Park, Tamar Geller, Priyadarshini Panda
• Abstract summary: We propose neuromorphic data augmentation (NDA) for event-based datasets. NDA significantly stabilizes the SNN training and reduces the generalization gap between training and test performance. For the first time, we demonstrate the feasibility of unsupervised contrastive learning for SNNs.
• Score: 10.303676184878896
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: 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, NDA-based SNN achieves accuracy gain on CIFAR10-DVS and N-Caltech 101 by 10.1% and 13.7%, respectively.

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