新的SA-SAM方法在高度稀疏性下提高了深度神经网络的鲁棒性

作者 PulseAugur 编辑部 · [1 个来源] · 2026-09-15 04:00

研究人员开发了稀疏性自适应锐度感知最小化（SA-SAM）方法，这是一种提高深度神经网络在常见损坏下的鲁棒性的新方法，尤其是在高稀疏性水平下。SA-SAM根据稀疏性调整扰动半径以维持鲁棒性，并引入了幅度加权Hessian（MWH）进行模型剪枝。在CIFAR和ImageNet损坏数据集上的实验表明，SA-SAM在高稀疏性（80-90%）下实现了比现有方法更好的鲁棒性，同时保持了干净精度。 AI

影响增强了模型在可能存在噪声的实际条件下的部署鲁棒性和效率。

排序理由该集群包含一篇详细介绍深度神经网络新方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

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arXiv cs.CV TIER_1 English(EN) · Shiryu Ueno, Yoshikazu Hayashi, Kunihito Kato · 2026-09-15 04:00

Sparsity-Adaptive Sharpness-Aware Minimization

arXiv:2609.14274v1 Announce Type: new Abstract: Deploying deep neural networks in real-world settings requires models that are both compact and robust to common corruptions. However, at deployment-relevant high sparsity, standard pruning pipelines often degrade corruption robustn…

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Sparsity-Adaptive Sharpness-Aware Minimization

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