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 | Conference PDF | Archive PDF | Plain Text | 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 first 5 and 10 epochs, respectively. |