Decomposed Diffusion Sampler for Accelerating Large-Scale Inverse Problems
Authors: Hyungjin Chung, Suhyeon Lee, Jong Chul Ye
ICLR 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We conduct experiments on two distinguished applications accelerated MRI, and 3D CT reconstruction. For the former, we follow the evaluation protocol of Chung & Ye (2022) and test our method on the fast MRI knee dataset (Zbontar et al., 2018) on diverse sub-sampling patterns. |
| Researcher Affiliation | Academia | 1 Dept. of Bio & Brain Engineering, KAIST, 2 Kim Jae Chul Graduate School of AI, KAIST {hj.chung, suhyeon.lee, jong.ye}@kaist.ac.kr |
| Pseudocode | Yes | In the following tables, we list all the DDS algorithms used throughout the manuscript. For simplicity, we define CG(A, y, x, M) to be running M steps of conjugate gradient steps with initialization x. For completeness, we include a pseudo-code of the CG method in Algorithm. 1 that is used throughout the work. |
| Open Source Code | Yes | Code is available at https://github.com/ HJ-harry/DDS |
| Open Datasets | Yes | We conduct all PI experiments with fast MRI knee dataset (Zbontar et al., 2018). ... AAPM 2016 CT low-dose grand challenge data leveraged in Chung et al. (2022a; 2023b) is used. ... All medical data used in our experiments were publicly available and fully anonymized, ensuring the utmost respect for patient confidentiality. |
| Dataset Splits | No | While the paper mentions using a 'validation dataset' from which the test set is selected for MRI and finding parameters via 'grid search on 50 validation images' for TV, it does not provide explicit details about the training/validation/test splits (e.g., exact percentages or counts for training and validation sets) for its main experiments. |
| Hardware Specification | Yes | on a single commodity GPU (RTX 3090). |
| Software Dependencies | No | The paper mentions software components such as 'torch-radon (Ronchetti, 2020) package', 'U-Net implementation from ADM (Dhariwal & Nichol, 2021)', and 'sigpy.mri.app.Total Variation', but it does not specify exact version numbers for these or other key software dependencies like Python, PyTorch, or CUDA. |
| Experiment Setup | Yes | For all proposed methods, we employ M = 5, η = 0.15 for 19 NFE, η = 0.5 for 49 NFE, η = 0.8 for 99 NFE unless specified otherwise. ... train each model for 1M iterations with the batch size of 4, initial learning rate of 1e 4 on a single RTX 3090 GPU. |