EventRPG: Event Data Augmentation with Relevance Propagation Guidance
Mingyuan Sun Donghao Zhang Zongyuan Ge Jiaxu Wang Jia Li Zheng Fang Renjing Xu
Event camera, a novel bio-inspired vision sensor, has drawn a lot of
attention for its low latency, low power consumption, and high dynamic range.
Currently, overfitting remains a critical problem in event-based classification
tasks for Spiking Neural Network (SNN) due to its relatively weak spatial
representation capability. Data augmentation is a simple but efficient method
to alleviate overfitting and improve the generalization ability of neural
networks, and saliency-based augmentation methods are proven to be effective in
the image processing field. However, there is no approach available for
extracting saliency maps from SNNs. Therefore, for the first time, we present
Spiking Layer-Time-wise Relevance Propagation rule (SLTRP) and Spiking
Layer-wise Relevance Propagation rule (SLRP) in order for SNN to generate
stable and accurate CAMs and saliency maps. Based on this, we propose EventRPG,
which leverages relevance propagation on the spiking neural network for more
efficient augmentation. Our proposed method has been evaluated on several SNN
structures, achieving state-of-the-art performance in object recognition tasks
including N-Caltech101, CIFAR10-DVS, with accuracies of 85.62% and 85.55%, as
well as action recognition task SL-Animals with an accuracy of 91.59%. Our code
is available at https://github.com/myuansun/EventRPG.