AsyncDSB: Schedule-Asynchronous Diffusion Schrödinger Bridge for Image Inpainting
Authors: Zihao Han, Baoquan Zhang, Lisai Zhang, Shanshan Feng, Kenghong Lin, Guotao Liang, Yunming Ye, Joeq , Kolaye
AAAI 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on real-world datasets show that our Async DSB achieves superior performance, especially on FID with around 3% 14% improvement over state-of-the-art baseline methods. Extensive experiments conducted on two datasets (i.e., Celeb A-HQ and Places2) demonstrate the superior performance of the proposed Async DSB method in comparison to various state-of-the-art image inpainting methods. |
| Researcher Affiliation | Collaboration | 1 Harbin Institute of Technology, Shenzhen; 2 Shen Zhen Si Far Co., Ltd.; 3 Centre for Frontier AI Research; 4 Shengshu AI. EMAIL, EMAIL, Lisai EMAIL, EMAIL, EMAIL |
| Pseudocode | No | The paper describes the methodology using textual explanations and mathematical equations, but it does not include any clearly labeled pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide an explicit statement about the release of source code for the described methodology, nor does it include any links to code repositories. |
| Open Datasets | Yes | We validate our method and various baselines over two typical dataset: 1) Celeb A-HQ is a face dataset. We split this dataset by following standard dataset split (Liu et al. 2015). 2) Places2 is a scene image dataset, which split into 1.8 million training images and 36500 validation images, from which we randomly select 6000 images as test. |
| Dataset Splits | Yes | We split this dataset by following standard dataset split (Liu et al. 2015). Places2 is a scene image dataset, which split into 1.8 million training images and 36500 validation images, from which we randomly select 6000 images as test. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for running the experiments, such as GPU or CPU models. |
| Software Dependencies | No | The paper does not specify any particular software dependencies or their version numbers that are needed to replicate the experiment. |
| Experiment Setup | Yes | Following (Liu et al. 2023), T = 1000 is used. where λadv and λF M are hyper-parameters; where τ max and τ min are two hyper-parameters, which controls the asynchronous range of pixel schedule; In Figure 6, we conduct a parameter analysis on Places 2 from τ min = 0.001T to τ min = 0.9T and τ max = 0.001T to τ min = 0.9T. |