Image-to-Image Translation with Multi-Path Consistency Regularization
Authors: Jianxin Lin, Yingce Xia, Yijun Wang, Tao Qin, Zhibo Chen
IJCAI 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We conduct various experiments to demonstrate the effectiveness of our proposed methods, including face-toface translation, paint-to-photo translation, and deraining/de-noising translation. |
| Researcher Affiliation | Collaboration | Jianxin Lin1 , Yingce Xia3 , Yijun Wang2 , Tao Qin3 and Zhibo Chen1 1CAS Key Laboratory of Technology in Geo-spatial Information Processing and Application System, University of Science and Technology of China 2University of Science and Technology of China 3Microsoft Research Asia |
| Pseudocode | No | The paper describes its framework and training processes using mathematical equations and textual explanations, but it does not include a formal pseudocode block or algorithm listing. |
| Open Source Code | No | The paper does not provide any statement about releasing its source code or a link to a code repository. |
| Open Datasets | Yes | For multi-domain face-to-face translation, we use the Celeb A dataset [Liu et al., 2015]... For multi-domain paint-to-photo translation, we use the paintings and photographs collected by [Zhu et al., 2017]... We use the raining images and original images collected by [Fu et al., 2017; Yang et al., 2017]. |
| Dataset Splits | No | The paper refers to 'training data' and a 'test set' but does not specify the exact percentages, sample counts, or methodology used for the train/validation/test dataset splits needed for reproduction. |
| Hardware Specification | Yes | All the models are trained on one NVIDIA K40 GPU for one day. |
| Software Dependencies | No | The paper mentions using 'Adam optimizer' but does not specify version numbers for any key software components, libraries, or frameworks (e.g., Python, PyTorch/TensorFlow, CUDA versions). |
| Experiment Setup | Yes | We use Adam optimizer [Kingma and Ba, 2014] with learning rate 0.0001 for the first 10 epochs and linearly decay the learning rate every 10 epochs. The α in Eqn. (4) and Eqn. (7) is set to 0.1, and β in Eqn. (7) is also set to 0.1. |