Jun 20, 2022 · We found that SNNs trained with the appropriate temporal penalty settings are more robust against adversarial images than ANNs.
We found that SNNs trained with the appropriate temporal penalty settings are more robust against adversarial images than ANNs. Index Terms—spiking neural ...
Spiking neural networks (SNNs) more closely mimic the human brain than artificial neural networks (ANNs). For SNNs, time-to-first-spike (TTFS) encoding, ...
May 7, 2019 · In this work, we present, for the first time, a comprehensive analysis of the behavior of more bio-plausible networks, namely Spiking. Neural ...
They highlighted that specific input coding could improve the robustness of SNN. Up to now, Poisson coding, latency coding, and time-to-first-spike coding have ...
This work presents a comprehensive analysis of the behavior of more bio-plausible networks, namely Spiking Neural Network (SNN) under state-of-the-art ...
Based on our experiment, we can achieve a maximum 37.7% attack error reduction with 3.7% original accuracy loss. To the best of our knowledge, this is the first ...
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Apr 26, 2024 · Simultaneously, a multi-level firing SNN based on Squeeze-and-Excitation Network is introduced to improve the robustness of the classifier.
Robustness of spiking neural networks based on time-to-first-spike encoding against adversarial attacks. IEEE Transactions on. Circuits and Systems II ...
Robustness of spiking neural networks based on time-to-first-spike encoding against adversarial attacks. IEEE Transactions on. Circuits and Systems II ...