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..
Multi-source Domain Adaptation for Semantic Segmentation
Authors: Sicheng Zhao, Bo Li, Xiangyu Yue, Yang Gu, Pengfei Xu, Runbo Hu, Hua Chai, Kurt Keutzer
NeurIPS 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments from synthetic GTA and SYNTHIA to real Cityscapes and BDDS datasets demonstrate that the proposed MADAN model outperforms state-of-the-art approaches. |
| Researcher Affiliation | Collaboration | Sicheng Zhao1 , Bo Li23 , Xiangyu Yue1 , Yang Gu2, Pengfei Xu2, Runbo Hu2, Hua Chai2, Kurt Keutzer1 1University of California, Berkeley, USA 2Didi Chuxing, China 3Harbin Institute of Technology, China |
| Pseudocode | No | The paper provides a high-level framework diagram (Figure 1) and describes the components and training process in text, but it does not include any pseudocode or algorithm blocks. |
| Open Source Code | Yes | Our source code is released at: https://github.com/Luodian/MADAN. |
| Open Datasets | Yes | In our adaptation experiments, we use synthetic GTA [18] and SYNTHIA [19] datasets as the source domains and real Cityscapes [15] and BDDS [72] datasets as the target domains. |
| Dataset Splits | No | The paper mentions training images are cropped to 400x400 during the training of pixel-level adaptation for 20 epochs, but it does not specify a separate validation dataset split, percentage, or number of samples used for validation. |
| Hardware Specification | Yes | In our experiments, MADAN is trained on 4 NVIDIA Tesla P40 GPUs for 40 hours using two source domains which is about twice the training time as on a single source. |
| Software Dependencies | No | The paper states: "The network is implemented in Py Torch and trained with Adam optimizer [75]..." While PyTorch and Adam optimizer are mentioned, specific version numbers for these software dependencies are not provided. |
| Experiment Setup | Yes | The network is implemented in Py Torch and trained with Adam optimizer [75] using a batch size of 8 with initial learning rate 1e-4. All the images are resized to 600 1080, and are then cropped to 400 400 during the training of the pixel-level adaptation for 20 epochs. SAD and CCD are frozen in the ο¬rst 5 and 10 epochs, respectively. |