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..
Binarized Diffusion Model for Image Super-Resolution
Authors: Zheng Chen, Haotong Qin, Yong Guo, Xiongfei Su, Xin Yuan, Linghe Kong, Yulun Zhang
NeurIPS 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Comprehensive experiments demonstrate that our BI-Diff SR outperforms existing binarization methods. |
| Researcher Affiliation | Academia | 1Shanghai Jiao Tong University, 2ETH Zürich, 3Max Planck Institute for Informatics, 4Westlake University |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | Code is released at: https://github.com/zhengchen1999/BI-Diff SR. |
| Open Datasets | Yes | We take DIV2K [59] and Flickr2K [33] as the training dataset. |
| Dataset Splits | No | The paper states training and testing datasets (DIV2K, Flickr2K for training; Manga109 for testing in ablation study), but does not explicitly define a separate validation dataset split with proportions or sample counts. |
| Hardware Specification | Yes | Our model is implemented based on Py Torch [47] with two Nvidia A100-80G GPUs. |
| Software Dependencies | No | The paper mentions PyTorch as the implementation framework but does not specify its version or any other software dependencies with version numbers. |
| Experiment Setup | Yes | For the noise estimation network, we set the encoder and decoder level to 4. ... We train models with the L1 loss. We employ the Adam optimizer [22] with β1=0.9 and β2=0.99, and a learning rate of 1 10 4. The batch size is set to 16, with a total of 1,000K iterations. |