2 citations · 5 across the 10 of their papers we have counts for
6 papers · 1 filter
Toward End-to-End Bearing Fault Diagnosis for Industrial Scenarios with Spiking Neural Networks
Lin Zuo, Yongqi Ding, Mengmeng Jing +3
This paper explores the application of spiking neural networks (SNNs), known for their low-power binary spikes, to bearing fault diagnosis, bridging the gap between high-performanc…
Temporal Reversal Regularization for Spiking Neural Networks: Hybrid Spatio-Temporal Invariance for Generalization
Lin Zuo, Yongqi Ding, Wenwei Luo +2
Spiking neural networks (SNNs) have received widespread attention as an ultra-low power computing paradigm. Recent studies have shown that SNNs suffer from severe overfitting, whic…
Multi-Bit Mechanism: A Novel Information Transmission Paradigm for Spiking Neural Networks
Yongjun Xiao, Xianlong Tian, Yongqi Ding +3
Since proposed, spiking neural networks (SNNs) gain recognition for their high performance, low power consumption and enhanced biological interpretability. However, while bringing…
Self-Distillation Learning Based on Temporal-Spatial Consistency for Spiking Neural Networks
Lin Zuo, Yongqi Ding, Mengmeng Jing +2
Spiking neural networks (SNNs) have attracted considerable attention for their event-driven, low-power characteristics and high biological interpretability. Inspired by knowledge d…
Flexible ViG: Learning the Self-Saliency for Flexible Object Recognition
Lin Zuo, Kunshan Yang, Xianlong Tian +3
Existing computer vision methods mainly focus on the recognition of rigid objects, whereas the recognition of flexible objects remains unexplored. Recognizing flexible objects pose…
Shrinking Your TimeStep: Towards Low-Latency Neuromorphic Object Recognition with Spiking Neural Network
Yongqi Ding, Lin Zuo, Mengmeng Jing +2
Neuromorphic object recognition with spiking neural networks (SNNs) is the cornerstone of low-power neuromorphic computing. However, existing SNNs suffer from significant latency,…