Towards Unsupervised Deformable-Instances Image-to-Image Translation
Authors: Sitong Su, Jingkuan Song, Lianli Gao, Junchen Zhu
IJCAI 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on four datasets demonstrate the significant advantages of our MGD-GAN over existing methods both quantitatively and qualitatively. |
| Researcher Affiliation | Academia | Sitong Su , Jingkuan Song , Lianli Gao and Junchen Zhu Center for Future Media, University of Electronic Science and Technology of China |
| Pseudocode | No | The paper describes the model architecture and equations but does not include any clearly labeled pseudocode or algorithm blocks. |
| Open Source Code | Yes | The code could be available at https://github.com/sitongsu/ MGD GAN |
| Open Datasets | Yes | MS COCO [Lin et al., 2014]: Three domain pairs, sheep&giraffe, elephant&zebra and bottle&cup, are selected from MS COCO. Masks of each instance are provided. Multi-Human Parsing [Zhao et al., 2018]: Each image in MHP contains at least two persons (average 3) in crowd scenes. |
| Dataset Splits | No | The paper mentions training epochs and batch size but does not specify explicit training, validation, and test dataset splits (e.g., percentages or sample counts). |
| Hardware Specification | Yes | All experiments are conducted on a NVIDIA Titan Xp GPU. |
| Software Dependencies | No | The paper mentions using Adam optimizer and Mask-RCNN, but it does not provide specific version numbers for software dependencies like deep learning frameworks (e.g., PyTorch, TensorFlow) or their specific versions. |
| Experiment Setup | Yes | We set batch size N = 2 for training. The mask, image and the fine-tuning part are trained for 100, 200 and 50 epochs, respectively. For hyper-parameters, we set λpenalty as 0.1, λconst and λreg as 1, λpc, λcom, λfmap, λvgg and λfeat as 10. The Adam optimizer is adopted with β1 = 0.5 and β2 = 0.999 and the learning rate lr = 0.002. |