Notice: 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. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Spatial-Contextual Discrepancy Information Compensation for GAN Inversion
Authors: Ziqiang Zhang, Yan Yan, Jing-Hao Xue, Hanzi Wang
AAAI 2024 | Venue PDF | 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. |