Learned imaging with constraints and uncertainty quantification
| Title | Learned imaging with constraints and uncertainty quantification |
| Publication Type | Conference |
| Year of Publication | 2019 |
| Authors | Felix J. Herrmann, Ali Siahkoohi, Gabrio Rizzuti |
| Conference Name | Neural Information Processing Systems (NeurIPS) |
| Month | 12 |
| Keywords | constraint, deep learning, Imaging, Uncertainty quantification |
| Abstract | We outline new approaches to incorporate ideas from convolutional networks into wave-based least-squares imaging. The aim is to combine hand-crafted constraints with deep convolutional networks allowing us to directly train a network capable of generating samples from the posterior. The main contributions include combination of weak deep priors with hard handcrafted constraints and a possible new way to sample the posterior. |
| Notes | (NIPS, Vancouver) |
| URL | https://slim.gatech.edu/Publications/Public/Conferences/NIPS/2019/herrmann2019NIPSliwcuc/herrmann2019NIPSliwcuc.html |
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| Citation Key | herrmann2019NIPSliwcuc |