Online Deep Equilibrium Learning for Regularization by Denoising
Authors: Jiaming Liu, Xiaojian Xu, Weijie Gan, shirin shoushtari, Ulugbek Kamilov
NeurIPS 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our numerical results suggest the potential improvements in training/testing complexity due to ODER on three distinct imaging applications. |
| Researcher Affiliation | Academia | Jiaming Liu Washington University in St. Louis jiaming.liu@wustl.edu Xiaojian Xu Washington University in St. Louis xiaojianxu@wustl.edu Weijie Gan Washington University in St. Louis weijie.gan@wustl.edu Shirin Shoushtari Washington University in St. Louis s.shirin@wustl.edu Ulugbek S. Kamilov Washington University in St. Louis kamilov@wustl.edu |
| Pseudocode | No | The paper describes the algorithms in text, for example, the forward and backward passes, but does not provide a formal pseudocode block or algorithm listing. |
| Open Source Code | Yes | The code for our numerical evaluation is available at: https://github.com/wustl-cig/ODER. |
| Open Datasets | Yes | In the simulation, we randomly extracted and cropped 400 slices of 416 416 images for training, 28 images for validation and 56 images for testing from Brecahad database [85]. We consider simulated data obtained from the clinically realistic CT images provided by Mayo Clinic for the low dose CT grand challenge [87]. The first dataset [28] provides 800 slices of 256 256 images for training and 50 slices for testing. The second dataset [91] contains a randomly selected 400 volumes of 320 320 10 images for training, and 32 volumes for testing. |
| Dataset Splits | Yes | In the simulation, we randomly extracted and cropped 400 slices of 416 416 images for training, 28 images for validation and 56 images for testing from Brecahad database [85]. |
| Hardware Specification | No | The paper states, '3. (d) Did you include the total amount of compute and the type of resources used (e.g., type of GPUs, internal cluster, or cloud provider)? [Yes] In the Supplement.' However, the provided text does not contain these specific details within the main body. |
| Software Dependencies | No | The paper mentions software like 'U-Net', 'Dn CNN', 'DRUNet', 'PyTorch implementation of Radon and IRadon 2 transform', 'Nesterov acceleration', 'Anderson acceleration', 'Adam', 'fminbound in the scipy.optimize toolbox', and 'Sig Py', but it does not provide specific version numbers for these software components or libraries. |
| Experiment Setup | Yes | During the training of both ODER and RED (DEQ), we use the Nesterov acceleration [80] for the forward pass and Anderson acceleration [83] for the backward pass. We also adopt the stopping criterion from [40,84] by setting residual tolerance to 10 3 for both forward and backward iterations (see supplement for additional details). |