Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Zero-Shot Image Restoration Using Denoising Diffusion Null-Space Model
Authors: Yinhuai Wang, Jiwen Yu, Jian Zhang
ICLR 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our experiments on several IR tasks reveal that DDNM outperforms other state-of-the-art zero-shot IR methods. We also demonstrate that DDNM+ can solve complex real-world applications, e.g., old photo restoration. and 4 EXPERIMENTS Our experiments consist of three parts. Firstly, we evaluate the performance of DDNM on five typical IR tasks and compare it with state-of-the-art zero-shot IR methods. Secondly, we experiment DDNM+ on three typical IR tasks to verify its improvements against DDNM. Thirdly, we show that DDNM and DDNM+ perform well on challenging real-world applications. |
| Researcher Affiliation | Academia | 1Peking University Shenzhen Graduate School, 2Peng Cheng Laboratory |
| Pseudocode | Yes | Algorithm 1 Sampling of DDNM and Algorithm 2 Sampling of DDNM+ |
| Open Source Code | Yes | Code is available at https://github.com/wyhuai/DDNM. |
| Open Datasets | Yes | We choose Image Net 1K and Celeb A-HQ 1K datasets with image size 256 256 for validation. |
| Dataset Splits | No | The paper uses pre-trained denoising networks and mentions using ImageNet 1K and Celeb A-HQ 1K for validation, but it does not specify the train/validation/test splits (percentages or counts) that were used for their specific experiments, nor does it provide citations to such predefined splits in a way that allows direct reproduction of the data partitioning. |
| Hardware Specification | Yes | on a single 2080Ti GPU with batch size 1 |
| Software Dependencies | No | The paper provides 'Pytorch-like codes' in Appendix E but does not specify version numbers for PyTorch or any other software dependencies. |
| Experiment Setup | Yes | We use DDIM as the base sampling strategy with η = 0.85, 100 steps, without classifier guidance, for all diffusion-based methods. and For fair comparison, we set T = 250, l = s = 20, r = 3 for DDNM+ while set T = 1000 for DDNM so that the total sampling steps and computational consumptions are roughly equal. |