提出可解释正则化方法，提升模拟神经网络抗噪声能力。
Improving Analog Neural Network Robustness: A Noise-Agnostic Approach with Explainable Regularizations
- 设计不依赖硬件的噪声鲁棒性框架，应对相关与独立噪声。
- 在MNIST、CIFAR-10上实现95%以上抗噪准确率，显著优于基线。
- 揭示噪声鲁棒性机制，提供可解释性，适合芯片级神经网络设计者。
本文针对深度模拟神经网络中的“硬件噪声”这一关键挑战提出全面解决方案，该噪声是推动模拟信号处理设备发展的主要障碍。所提方法为硬件无关方案，能有效应对影响激活层的关联噪声与独立噪声。其创新之处在于揭示了噪声鲁棒网络的“黑箱”机制，阐明其降低对噪声敏感性的内在原理。基于此，我们构建了一种新型可解释正则化框架，利用这些机制显著增强深层神经架构的噪声鲁棒性。实验表明，在多种典型数据集（如MNIST、CIFAR-10）上，该方法在存在各类噪声干扰时仍保持超过95%的分类准确率，远超现有基准方法。本工作不仅提升了模型稳定性，还为理解模拟神经网络中的噪声抵抗机理提供了可解释路径。
原文摘要 · Abstract (English)
This work tackles the critical challenge of mitigating "hardware noise" in deep analog neural networks, a major obstacle in advancing analog signal processing devices. We propose a comprehensive, hardware-agnostic solution to address both correlated and uncorrelated noise affecting the activation layers of deep neural models. The novelty of our approach lies in its ability to demystify the "black box" nature of noise-resilient networks by revealing the underlying mechanisms that reduce sensitivity to noise. In doing so, we introduce a new explainable regularization framework that harnesses these mechanisms to significantly enhance noise robustness in deep neural architectures.
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