初始化时剪枝未展开网络，提升MRI重建对分布偏移的鲁棒性。
Pruning Unrolled Networks (PUN) at Initialization for MRI Reconstruction Improves Generalization
- 在训练初始阶段剪枝未展开的深度重建网络
- 跨多种实验设置泛化能力显著优于传统密集网络
- 对分布内数据也有轻微性能提升，适合医疗图像重建场景
深度学习在图像重建任务中表现优异，但在测试时面对不同实验设置或分布偏移时性能可能下降。本研究证明，在训练初期剪枝深度图像重建网络可增强其对分布偏移的鲁棒性。特别地，针对加速磁共振成像的未展开重建架构，提出一种初始化时剪枝未展开网络（PUN）的方法。实验表明，相比传统密集网络，PUN在多种实验设置下具备更优泛化能力，甚至在分布内数据上也有轻微性能提升。
原文摘要 · Abstract (English)
Deep learning methods are highly effective for many image reconstruction tasks. However, the performance of supervised learned models can degrade when applied to distinct experimental settings at test time or in the presence of distribution shifts. In this study, we demonstrate that pruning deep image reconstruction networks at training time can improve their robustness to distribution shifts. In particular, we consider unrolled reconstruction architectures for accelerated magnetic resonance imaging and introduce a method for pruning unrolled networks (PUN) at initialization. Our experiments demonstrate that when compared to traditional dense networks, PUN offers improved generalization across a variety of experimental settings and even slight performance gains on in-distribution data.
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