arXiv cs.AI· Francesco Panetta, Federico Di Valerio, Michela Proietti·· 3 天前AI 评分20
基于显著性引导回放的深度脉冲神经网络持续学习
Saliency-Guided Replay for Continual Learning in Deep Spiking Neural Networks
AI 导读
研究将经验打包与回放（EPR）用于低延迟约束下的深度脉冲神经网络持续学习，结合 Batch Normalization Through Time 稳定的 VGG9 SNN 与 Spike Activation Maps（SAM），把脉冲活动解释聚合为空间注意力图，只回放信息量最大的图像块而非完整图像。
正文 · 原文
Abstract:Spiking Neural Networks (SNNs) are promising for energy-efficient neuromorphic computing, but they still suffer from catastrophic forgetting when learning tasks sequentially. This work investigates the integration of Experience Packing and Replay (EPR) as a memory-efficient continual learning strategy for deep SNNs under low-latency constraints. The proposed framework combines Batch Normalization Through Time stabilized VGG9 SNNs with Spike Activation Maps (SAM), which provide forward-only explanations based on spike activity. These explanations are temporally aggregated into spatial attention maps and used to select the most informative image patches for replay, instead of storing full images. Experiments on Split CIFAR10 and Split CIFAR100 show that SAM-guided EPR improves the accuracy-memory trade-off under tight memory budgets, and remains competitive while requiring substantially less storage.
| Comments: | Streaming Continual Learning Workshop. In post-proceeding of International Workshops of ECML PKDD 2026 |
| Subjects: | Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV); Neural and Evolutionary Computing (cs.NE) |
| Cite as: | arXiv:2610.17998 [cs.LG] |
| (or arXiv:2610.17998v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2610.17998 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Federico Di Valerio [view email]
[v1]
Tue, 13 Oct 2026 08:58:28 UTC (591 KB)
来源：arXiv cs.AI · arxiv.org