7 papers
Spike-PTSD: A Bio-Plausible Adversarial Example Attack on Spiking Neural Networks via PTSD-Inspired Spike Scaling
Lingxin Jin, Wei Jiang, Maregu Assefa Habtie +5
Spiking Neural Networks (SNNs) are energy-efficient and biologically plausible, ideal for embedded and security-critical systems, yet their adversarial robustness remains open. Exi…
Stable Spike: Dual Consistency Optimization via Bitwise AND Operations for Spiking Neural Networks
Yongqi Ding, Kunshan Yang, Linze Li +3
Although the temporal spike dynamics of spiking neural networks (SNNs) enable low-power temporal pattern capture capabilities, they also incur inherent inconsistencies that severel…
Dropout Prompt Learning: Towards Robust and Adaptive Vision-Language Models
Biao Chen, Lin Zuo, Mengmeng Jing +2
Dropout is a widely used regularization technique which improves the generalization ability of a model by randomly dropping neurons. In light of this, we propose Dropout Prompt Lea…
Synergy Between the Strong and the Weak: Spiking Neural Networks are Inherently Self-Distillers
Yongqi Ding, Lin Zuo, Mengmeng Jing +3
Brain-inspired spiking neural networks (SNNs) promise to be a low-power alternative to computationally intensive artificial neural networks (ANNs), although performance gaps persis…
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…