Pruning Unrolled Networks (PUN) at Initialization for MRI Reconstruction Improves Generalization
Journal:
arXiv
Published Date:
Dec 24, 2024
Abstract
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.
