Beyond Sparsity: Weight Location and Network Context in Pruned MRI Reconstruction
Pruning reduces the number of weights in magnetic resonance imaging (MRI) reconstruction networks. Equal sparsity, however, can retain weights with different computational roles and different compatibility with the trained network. We study these effects across 120 convolutional U-Net and vision transformer models using controlled edits evaluated before retraining. In U-Net, preserving high-resolution computation improves reconstruction at equal deletion counts across all tested sparsity levels. Equal-operation controls reveal additional sensitivity of the first convolution, which operation count alone cannot explain. In transformers, retained pretrained weight energy correlates with qual...
| Authors | Mohammed Wattad, Tamir Shor, Alexander M. Bronstein |
|---|---|
| Primary category | eess.IV |
| Published | 25 September 2026 |
| Last updated at source | 25 September 2026 |
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