Generative Adversarial Networks for Video-to-Video Domain Adaptation
Authors: Jiawei Chen, Yuexiang Li, Kai Ma, Yefeng Zheng3462-3469
AAAI 2020 | Conference PDF | Archive PDF | Plain Text | 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 {jiaweichen, vicyxli, kylekma, yefengzheng}@tencent.com |
| 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. |