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..
A Unified Conditional Framework for Diffusion-based Image Restoration
Authors: Yi Zhang, Xiaoyu Shi, Dasong Li, Xiaogang Wang, Jian Wang, Hongsheng Li
NeurIPS 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate our conditional framework on three challenging tasks: extreme low-light denoising, deblurring, and JPEG restoration, demonstrating its significant improvements in perceptual quality and the generalization to restoration tasks. |
| Researcher Affiliation | Collaboration | 1 CUHK MMLab 2 Snap Research 3 Centre for Perceptual and Interactive Intelligence 4 Shanghai AI Laboratory |
| Pseudocode | No | The paper describes its method in text and diagrams (Figure 1, Figure 2) but does not provide a formal pseudocode or algorithm block. |
| Open Source Code | Yes | https://zhangyi-3.github.io/project/UCDIR |
| Open Datasets | Yes | For extreme low-light denoising, we use the SID Sony dataset [7]... For deblurring, we follow Dv SR [50] to train and test on the Go Pro dataset. For JPEG restoration, we train on the Image Net and follow DDRM [26] to test on selected 1K evaluation images [37]. |
| Dataset Splits | No | The paper mentions training parameters and using a test set, but does not explicitly describe validation dataset splits, percentages, or counts. |
| Hardware Specification | Yes | The training process takes approximately three days to complete when utilizing 8 A100 GPUs. |
| Software Dependencies | No | The paper mentions optimizers (Adam W) and activation functions (Swish) but does not provide specific version numbers for software dependencies or libraries used for implementation. |
| Experiment Setup | Yes | We used the Adam W optimizer with a learning rate of 1 10 4, and EMA decay rate is 0.9999. In the training, we used the diffusion process with T = 2000 steps with the continuous noise level [10]. During the testing, the inference step is reduced to 50 with uniform interpolation. ...We train each task for 500k iterations with batch size 32. |