Spatial-Contextual Discrepancy Information Compensation for GAN Inversion
Authors: Ziqiang Zhang, Yan Yan, Jing-Hao Xue, Hanzi Wang
AAAI 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Both quantitative and qualitative experiments demonstrate that our proposed method achieves the excellent distortion-editability trade-off at a fast inference speed for both image inversion and editing tasks. Our code is available at https://github.com/ZzqLKED/SDIC. Experiments Experimental Settings Datesets. We evaluate our method on two domains: human faces and cars. |
| Researcher Affiliation | Academia | Ziqiang Zhang1, Yan Yan1*, Jing-Hao Xue2, Hanzi Wang1 1Xiamen University, China 2University College London, UK |
| Pseudocode | No | The paper describes its method and architecture in detail through text and diagrams (e.g., Figure 1) but does not include explicit pseudocode or algorithm blocks. |
| Open Source Code | Yes | Our code is available at https://github.com/ZzqLKED/SDIC. |
| Open Datasets | Yes | For the face domain, we adopt the widely-used FFHQ dataset (Karras, Laine, and Aila 2019) for training and the Celeb A-HQ dataset (Karras et al. 2017; Liu et al. 2015) for testing. For the car domain, we used the Stanford car dataset (Krause et al. 2013) for training and testing. |
| Dataset Splits | No | The paper mentions training and testing datasets but does not explicitly provide information on validation dataset splits or percentages. |
| Hardware Specification | Yes | Our model is trained 100,000 steps on the NVIDIA Ge Force RTX 3080 GPU. |
| Software Dependencies | No | The paper mentions using specific models and optimizers (e.g., 'ranger optimizer', 'Arc Face model', 'Res Net-50 model', 'Style GAN generator', 'e4e encoder') but does not provide specific version numbers for software dependencies or programming frameworks. |
| Experiment Setup | Yes | λLP IP S, λID, and λedit are empirically set to 0.8, 0.2, and 0.5, respectively. We use the ranger optimizer (Yong et al. 2020) with a learning rate of 0.001 and a batch size of 2. Our model is trained 100,000 steps on the NVIDIA Ge Force RTX 3080 GPU. |