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.
I$^2$SB: Image-to-Image Schrödinger Bridge
Authors: Guan-Horng Liu, Arash Vahdat, De-An Huang, Evangelos Theodorou, Weili Nie, Anima Anandkumar
ICML 2023 | Venue PDF | LLM Run Details | Input Tokens: 26,848 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,353 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 | We validate I2SB in solving various image restoration tasks, including inpainting, super-resolution, deblurring, and JPEG restoration on Image Net 256 256 and show that I2SB surpasses standard conditional diffusion models with more interpretable generative processes. |
| Researcher Affiliation | Collaboration | 1Georgia Institute of Technology 2NVIDIA 3California Institute of Technology. |
| Pseudocode | Yes | Algorithm 1 Training |
| Open Source Code | Yes | Project page and codes: https://i2sb.github.io/. |
| Open Datasets | Yes | on Image Net 256 256 (Deng et al., 2009) |
| Dataset Splits | Yes | Following the baselines (Saharia et al., 2022; Song et al., 2022), we report super-resolution results on the full Image Net validation set and report the remaining results on a 10k validation subset. |
| Hardware Specification | No | The inference time is measured on a V100 16G. (This is mentioned only for inference time in a specific table, not as the general hardware used for training or all experiments, so it's not a full specification.) |
| Software Dependencies | No | Official Pytorch implementation of our I2SB can be found in https://github.com/NVlabs/I2SB. (This refers to the implementation language/framework but doesn't give specific version numbers for dependencies). |
| Experiment Setup | Yes | By default, we use 1000 sampling time steps for all tasks with quadratic discretization (Song et al., 2020a). |