English(EN) PAFS: Perturbation-Aware Firing Scaffolds for Robust Event-Based SNNs

新方法增强了事件驱动脉冲神经网络的鲁棒性

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-23 09:34

研究人员开发了扰动感知发放脚手架（PAFS），一种用于事件驱动脉冲神经网络（SNNs）的新型训练后校准方法。PAFS通过分析晚期发放轨迹来解决SNNs对事件丢失或时间戳抖动等扰动的敏感性问题。该方法将这些轨迹与适配器锚点的类别概率残差相关联，并使用奇异值分解提取残差对齐的通道方向。当应用于损坏数据时，PAFS将发放轨迹投影到该脚手架上以纠正logit预测，在各种数据集和架构上，可将准确率提高多达3.65个百分点，同时仅增加极少的训练参数。 AI

影响在现实条件下增强了事件驱动人工智能系统的可靠性，并有可能得到更广泛的应用。

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arXiv cs.NE (Neural & Evolutionary) TIER_1 English(EN) · Thomas Bräunl · 2026-10-23 09:34

PAFS：用于鲁棒事件驱动SNN的扰动感知触发支架

Event-based spiking neural networks (SNNs) encode information through temporal firing dynamics, but the same stateful computation makes their predictions sensitive to event loss, timestamp jitter, and speed variation. These perturbations alter evidence accumulation, yet late-laye…

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PAFS：用于鲁棒事件驱动SNN的扰动感知触发支架

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