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..
Image-to-Image Translation with Multi-Path Consistency Regularization
Authors: Jianxin Lin, Yingce Xia, Yijun Wang, Tao Qin, Zhibo Chen
IJCAI 2019 | Venue PDF | 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 ο¬rst 10 epochs and linearly decay the learning rate every 10 epochs. The Ξ± in Eqn. (4) and Eqn. (7) is set to 0.1, and Ξ² in Eqn. (7) is also set to 0.1. |