Temporal Reversed Training for Spiking Neural Networks with Generalized Spatio-Temporal Representation
<|reference_start|>Temporal Reversed Training for Spiking Neural Networks with Generalized Spatio-Temporal Representation: Spiking neural networks (SNNs) have received widespread attention as an ultra-low energy computing paradigm. Recent studies have focused on improving the feature extraction capability of SNNs, but they suffer from inefficient inference and suboptimal performance. In this paper, we propose a simple yet effective temporal reversed training (TRT) method to optimize the spatio-temporal performance of SNNs and circumvent these problems. We perturb the input temporal data by temporal reversal, prompting the SNN to produce original-reversed consistent output logits and to learn perturbation-invariant representations. For static data without temporal dimension, we generalize this strategy by exploiting the inherent temporal property of spiking neurons for spike feature temporal reversal. In addition, we utilize the lightweight ``star operation" (element-wise multiplication) to hybridize the original and temporally reversed spike firing rates and expand the implicit dimensions, which serves as spatio-temporal regularization to further enhance the generalization of the SNN. Our method involves only an additional temporal reversal operation and element-wise multiplication during training, thus incurring negligible training overhead and not affecting the inference efficiency at all. Extensive experiments on static/neuromorphic object/action recognition, and 3D point cloud classification tasks demonstrate the effectiveness and generalizability of our method. In particular, with only two timesteps, our method achieves 74.77\% and 90.57\% accuracy on ImageNet and ModelNet40, respectively.<|reference_end|>
@article{zuo2024temporal,
    title={Temporal Reversed Training for Spiking Neural Networks with Generalized
  Spatio-Temporal Representation},
    author={Lin Zuo, Yongqi Ding, Wenwei Luo, Mengmeng Jing, Xianlong Tian,
  Kunshan Yang},
    journal={arXiv preprint arXiv:2408.09108},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.09108},
    primaryClass={cs.AI cs.CV}
}