SDGAN: Disentangling Semantic Manipulation for Facial Attribute Editing
Authors: Wenmin Huang, Weiqi Luo, Jiwu Huang, Xiaochun Cao
AAAI 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We extensively evaluate our method on the Celeb A-HQ database, providing both qualitative and quantitative analyses. Our results establish that SDGAN significantly outperforms state-of-the-art techniques, showcasing the effectiveness of our approach. |
| Researcher Affiliation | Academia | 1 School of Computer Science and Engineering, Sun Yat-sen University, China 2 Shenzhen Key Laboratory of Media Security, Shenzhen University, China 3 School of Cyber Science and Technology, Sun Yat-sen University, China |
| Pseudocode | No | The paper describes its methodology and training objectives in textual form and through mathematical equations, but it does not include any clearly labeled 'Pseudocode' or 'Algorithm' blocks. |
| Open Source Code | Yes | The code implementing our model is available at https://github.com/sysuhuangwenmin/SDGAN. |
| Open Datasets | Yes | Like previous methods (Li et al. 2021b; Pehlivan, Dalva, and Dundar 2023), we evaluate our method on Celeb A-HQ (Karras et al. 2018), which comprises 30,000 facial images with attribute annotations. |
| Dataset Splits | No | Following (Li et al. 2021b), we split Celeb A-HQ into a test set of 3,000 images and a training set of 27,000 images. The paper explicitly mentions training and test splits but does not provide specific details for a separate validation set. |
| Hardware Specification | No | The paper does not provide specific hardware details such as GPU models, CPU types, or memory amounts used for running its experiments. |
| Software Dependencies | No | The paper does not specify any software dependencies with version numbers (e.g., Python, PyTorch, TensorFlow versions, or CUDA versions) required to replicate the experiment. |
| Experiment Setup | No | The paper describes the overall framework and loss functions but does not provide specific experimental setup details such as concrete hyperparameter values (e.g., learning rate, batch size, number of epochs, optimizer settings) in the main text. |