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..
ResDiff: Combining CNN and Diffusion Model for Image Super-resolution
Authors: Shuyao Shang, Zhengyang Shan, Guangxing Liu, LunQian Wang, XingHua Wang, Zekai Zhang, Jinglin Zhang
AAAI 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The extensive experiments on multiple benchmark datasets demonstrate that Res Diff outperforms previous diffusion-based methods in terms of shorter model convergence time, superior generation quality, and more diverse samples. |
| Researcher Affiliation | Academia | Shuyao Shang 1, Zhengyang Shan 1, Guangxing Liu 1, Lun Qian Wang 2, Xing Hua Wang 2, Zekai Zhang 3, Jinglin Zhang 1 1 Shandong University 2 Linyi University 3 Qilu University of Technology |
| Pseudocode | Yes | Algorithm 1: Res Diff Inference |
| Open Source Code | No | The paper does not provide any concrete links or explicit statements about the release of its source code. |
| Open Datasets | Yes | Experiments on two face datasets (FFHQ and Celeb A) and two general datasets (Div2k and Urban100) demonstrate that Res Diff not only accelerates the model s convergence speed but also generates more fine-grained images. |
| Dataset Splits | Yes | Our Res Diff is trained solely on the provided training data to guarantee a fair comparison. |
| Hardware Specification | No | The paper mentions 'Due to equipment limitations' in the conclusion but does not specify the hardware (GPU/CPU models, memory, etc.) used for running the experiments. |
| Software Dependencies | No | The paper does not explicitly list specific software dependencies with version numbers (e.g., Python, PyTorch, CUDA versions). |
| Experiment Setup | No | The supplementary material contains detailed information about the training process, hyperparameters, and other relevant details. |