Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
Optimal Transport-Guided Conditional Score-Based Diffusion Model
Authors: Xiang Gu, Liwei Yang, Jian Sun, Zongben Xu
NeurIPS 2023 | Venue PDF | LLM Run Details | Input Tokens: 19,213 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 5,490 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on unpaired super-resolution and semi-paired image-to-image translation demonstrated the effectiveness of the proposed OTCS model. |
| Researcher Affiliation | Academia | 1 School of Mathematics and Statistics, Xi an Jiaotong University, Xi an, China 2 Pazhou Laboratory (Huangpu), Guangzhou, China 3 Peng Cheng Laboratory, Shenzhen, China EMAIL EMAIL |
| Pseudocode | Yes | The pseudo-code for training uĻ, vĻ is given in Appendix A. ... The pseudo-code of the training algorithm is given in Appendix A. |
| Open Source Code | Yes | Code is available at https://github.com/XJTU-XGU/OTCS. |
| Open Datasets | Yes | We conduct extensive experiments on unpaired super-resolution and semi-paired I2I tasks... Celeb A dataset [41]. ... images of cat, fox, and leopard from AFHQ [48] dataset... translation from MNIST [49] to Chinese-MNIST [50]. |
| Dataset Splits | No | The paper mentions splitting the dataset into A1, B1, C1 and training on A0, B1, and testing on C0, but it does not explicitly mention a 'validation' split or set. |
| Hardware Specification | No | The paper does not provide specific hardware details such as GPU or CPU models used for running experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers (e.g., Python 3.x, PyTorch 1.x) that were used for the implementation. |
| Experiment Setup | Yes | M = 0.2 in experiments. ... where Ļ is set to 0.1. |