Assessing a Single Image in Reference-Guided Image Synthesis
Authors: Jiayi Guo, Chaoqun Du, Jiangshan Wang, Huijuan Huang, Pengfei Wan, Gao Huang753-761
AAAI 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Empirical results on various datasets demonstrate that RISA is highly consistent with human preference and transfers well across models. |
| Researcher Affiliation | Collaboration | Jiayi Guo1, Chaoqun Du1, Jiangshan Wang2, Huijuan Huang3, Pengfei Wan3, Gao Huang1,4* 1Department of Automation, BNRist, Tsinghua University, Beijing, China 2Beijing University of Posts and Telecommunications, Beijing, China 3Y-tech, Kuaishou Technology 4Beijing Academy of Artificial Intelligence, Beijing, China |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described. |
| Open Datasets | Yes | We conduct our experiments on five datasets: Yosemite (Zhu et al. 2017), Celeb A-HQ (Karras et al. 2017), AFHQ (Choi et al. 2020), LSUN Church and Bedroom (Yu et al. 2015), all at the resolution of 256x256. |
| Dataset Splits | No | The paper describes how its training data is acquired ('Finally, for each dataset, 16k images with 16 different quality scores (k/16, k = 1, 2, ..., 15) is obtained as the training images') and describes 'testing samples' for human judgments, but it does not specify a conventional train/validation/test split for the RISA model itself. |
| Hardware Specification | Yes | The model is trained for 100 epochs using a single NVIDIA RTX 2080Ti GPU. |
| Software Dependencies | No | The paper states 'RISA is implemented in Py Torch (Paszke et al. 2019)' but does not provide specific version numbers for PyTorch or other software dependencies. |
| Experiment Setup | Yes | The batch size is set to 4 and the model is trained for 100 epochs using a single NVIDIA RTX 2080Ti GPU. We use the Adam (Kingma and Ba 2014) with β1 = 0 and β2 = 0.99. The weight decay and the learning rate are set to 10^-4. The weights of all modules are initialized using He initialization (He et al. 2015) and all bias are set to zero. |