Research map / Optimization / Optimizers
Differentiable Linearized ADMM
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use D-LADMM with 15 layers instead of LADMM with 1,500 iterations | Image denoising · Lena, 10% noise | PSNR: 33.2 -> 35.1 | Helped |
| Use D-LADMM instead of LADMM | Image denoising · Waterloo BragZone Greyscale set2, 10% noise | PSNR | Mixed |
| Use D-LADMM instead of ADMM-Net | Brain MR image recovery · 20% sampling | PSNR: 29.7 -> 31.2 | Helped |
| Use D-LADMM instead of ADMM-Net | Brain MR image recovery · 30% sampling | PSNR: 31.8 -> 33.5 | Helped |
| Train D-LADMM rather than ADMM-Net | Brain MR image recovery · Training on 100 images | training time: 17 hours -> 7.5 hours | Helped |
| Use D-LADMM rather than LADMM | Synthetic sparse recovery · Simulation dataset | NMSE | Helped |
| Choose a smaller lambda for LADMM | LADMM · Synthetic sparse recovery | NMSE | Helped |
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