ComGAN: Unsupervised Disentanglement and Segmentation via Image Composition
Authors: Rui Ding, Kehua Guo, Xiangyuan Zhu, Zheng Wu, Liwei Wang
NeurIPS 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results show that (i) Com GAN s network architecture effectively avoids trivial solutions without any supervised information and regularization; (ii) DS-Com GAN achieves remarkable results and outperforms existing semi-supervised and weakly supervised methods by a large margin in both the image disentanglement and unsupervised segmentation tasks. It implies that the redesign of Com GAN is a possible direction for future unsupervised work.1 |
| Researcher Affiliation | Academia | Rui Ding Central South University ruiding@csu.edu.cn Kehua Guo Central South University guokehua@csu.edu.cn Xiangyuan Zhu Central South University zhuxiangyuan@csu.edu.cn Zheng Wu Central South University wuzhenghuse@gmail.com Liwei Wang Central South University wang.liwei@csu.edu.cn |
| Pseudocode | No | No pseudocode or algorithm blocks (e.g., clearly labeled 'Algorithm' or 'Pseudocode' sections) were found in the paper. |
| Open Source Code | Yes | Corresponding author 1Code and data are available at https://github.com/Ruiding1/Com GAN |
| Open Datasets | Yes | The experiments are conducted on five fine-grained image datasets and a multi-object dataset: CUB [39], FS-100 [40], Stanford-Cars [41]. Stanford-Dogs [41], Flowers [42], CLEVR6 [43]. |
| Dataset Splits | No | No explicit details on validation dataset splits (e.g., specific percentages or sample counts for a validation set) were provided in the main text of the paper. |
| Hardware Specification | No | No specific hardware details (e.g., GPU models, CPU types, or cloud computing instances with specifications) used for running experiments were mentioned in the paper. |
| Software Dependencies | No | No specific software dependencies with version numbers (e.g., 'PyTorch 1.9', 'Python 3.8') were provided in the paper. |
| Experiment Setup | No | The paper mentions loss functions and the hyperparameter β (with details in the Appendix C.1) and λ (implicitly in text), but does not provide concrete numerical values for core training hyperparameters such as learning rate, batch size, or number of epochs in the main text. |