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Neuromorphic vision sensors (event cameras) are inherently suitable forspiking neural networks (SNNs) and provide novel neuromorphic vision data forthis biomimetic model. Due to the spatiotemporal characteristics, novel dataaugmentations are required to process the unconventional visual signals ofthese cameras. In this paper, we propose a novel Event SpatioTemporal Fragments(ESTF) augmentation method. It preserves the continuity of neuromorphic data bydrifting or inverting fragments of the spatiotemporal event stream to simulatethe disturbance of brightness variations, leading to more robust spiking neuralnetworks. Extensive experiments are performed on prevailing neuromorphicdatasets. It turns out that ESTF provides substantial improvements over puregeometric transformations and outperforms other event data augmentationmethods. It is worth noting that the SNNs with ESTF achieve thestate-of-the-art accuracy of 83.9\% on the CIFAR10-DVS dataset.

Training Robust Spiking Neural Networks on Neuromorphic Data with Spatiotemporal Fragments