通过迭代噪声提升模型重建精度与抗扰能力
SGD Jittering: A Training Strategy for Robust and Accurate Model-Based Architectures
- 训练时逐轮注入噪声，增强模型鲁棒性
- 在地震反卷积和MRI重建中显著提升泛化性能
- 适合医疗影像等安全敏感场景的模型训练
逆问题旨在从受污染或扰动的测量中重建未知数据。尽管多数研究聚焦于提升重建质量，但在安全关键应用中，泛化准确性和鲁棒性同样重要。基于模型的架构（MBAs）如循环展开方法具有更强可解释性且重建效果更优。实证表明，MBAs比黑箱求解器更具鲁棒性，但其准确率-鲁棒性权衡仍待深入探索。本文提出一种简单有效的MBAs训练策略——SGD抖动，在重建过程中逐次迭代注入噪声。理论证明，该方法不仅比标准均方误差训练具有更好的泛化能力，且对平均情况攻击更具鲁棒性。在去噪模拟、地震反卷积和单线圈MRI重建任务中验证了有效性。SGD抖动及其SPGD扩展版本在分布外数据上产生更清晰的重建结果，并增强了对抗攻击的抵抗力。
原文摘要 · Abstract (English)
Inverse problems aim to reconstruct unseen data from corrupted or perturbed measurements. While most work focuses on improving reconstruction quality, generalization accuracy and robustness are equally important, especially for safety-critical applications. Model-based architectures (MBAs), such as loop unrolling methods, are considered more interpretable and achieve better reconstructions. Empirical evidence suggests that MBAs are more robust to perturbations than black-box solvers, but the accuracy-robustness tradeoff in MBAs remains underexplored. In this work, we propose a simple yet effective training scheme for MBAs, called SGD jittering, which injects noise iteration-wise during reconstruction. We theoretically demonstrate that SGD jittering not only generalizes better than the standard mean squared error training but is also more robust to average-case attacks. We validate SGD jittering using denoising toy examples, seismic deconvolution, and single-coil MRI reconstruction. Both SGD jittering and its SPGD extension yield cleaner reconstructions for out-of-distribution data and demonstrates enhanced robustness against adversarial attacks.
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