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..
Generative Adversarial Networks for Video-to-Video Domain Adaptation
Authors: Jiawei Chen, Yuexiang Li, Kai Ma, Yefeng Zheng3462-3469
AAAI 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results demonstrate that the adapted colonoscopic video generated by our Video GAN can significantly boost the segmentation accuracy, i.e., an improvement of 5%, of colorectal polyps on multicentre datasets. |
| Researcher Affiliation | Industry | Jiawei Chen, Yuexiang Li, Kai Ma, Yefeng Zheng You Tu Lab, Tencent, Shenzhen, China EMAIL |
| Pseudocode | No | The paper provides network diagrams but no structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide an explicit statement about the release of its source code or a link to a code repository. |
| Open Datasets | Yes | The publicly available colonoscopic video datasets, i.e., CVC-Clinic4 (V azquez et al. 2017) and ETISLarib5 (Silva et al. 2014), are selected for our experiments. |
| Dataset Splits | No | The paper does not explicitly provide specific dataset split information for training, validation, or testing, nor does it specify the methodology for such splits. |
| Hardware Specification | Yes | The network is trained with a minibatch size of 1 on one GPU (Tesla P40 with 24 GB memory). |
| Software Dependencies | No | The proposed Video GAN is implemented using Py Torch. The paper does not provide specific version numbers for PyTorch or other software dependencies. |
| Experiment Setup | Yes | The Adam solver (Kingma and Ba 2014) with betas = (0.5, 0.999) is adopted for the optimization of Video GAN. The network is trained with a minibatch size of 1 on one GPU (Tesla P40 with 24 GB memory). The initial learning rate is set to 0.0002. The proposed Video GAN yields visually satisfactory translated frames after 200 training epochs. |