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..
Diffusion Priors for Variational Likelihood Estimation and Image Denoising
Authors: Jun Cheng, Shan Tan
NeurIPS 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments and analyses on diverse real-world datasets demonstrate the effectiveness of our method. |
| Researcher Affiliation | Academia | Jun Cheng, Shan Tan School of Artificial Intelligence and Automation, Huazhong University of Science and Technology EMAIL, EMAIL |
| Pseudocode | Yes | Algorithm 1 Difusion priors-based variational image denoising |
| Open Source Code | Yes | Code is available at https://github.com/HUST-Tan/Diffusion VI. |
| Open Datasets | Yes | We consider several real-world denoising datasets to evaluate our method, including SIDD [1], Poly U [47], CC [30], and FMDD [51]. |
| Dataset Splits | No | The paper mentions 'SIDD validation' and dataset sizes, but does not specify explicit training/validation/test splits (e.g., percentages or sample counts) for all datasets used. |
| Hardware Specification | Yes | All experiments are conducted on Nvidia 2080Ti GPU. |
| Software Dependencies | No | The paper mentions using a pre-trained diffusion model but does not specify versions for core software libraries or dependencies like Python, PyTorch, or CUDA. |
| Experiment Setup | Yes | The total diffusion steps are 1000 by default, i.e., t [1, , 1000]. We choose α = 1 and Gaussian kernel size l = 9. The hyperparameters β and s for different datasets are summarized in Table 1. Different α/β represent the rough estimation of the prior precision for noises in different datasets, and Gaussian kernel scale s controls the range of local spatial correlation. The temperature γ is set to 1/5 for all datasets and will be ablated in the sequel. |