Joint Adversarial Learning for Domain Adaptation in Semantic Segmentation
Authors: Yixin Zhang, Zilei Wang6877-6884
AAAI 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The extensive experiments on two widely used benchmarks show that our method can bring considerable performance improvement over different baseline methods, which well demonstrates the effectiveness of our method in the output space adaptation. |
| Researcher Affiliation | Academia | Yixin Zhang, Zilei Wang Department of Automation, University of Science and Technology of China zhyx12@mail.ustc.edu.cn, zlwang@ustc.edu.cn |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any concrete access information (e.g., repository link, explicit statement of code release) for the source code of the methodology. |
| Open Datasets | Yes | Datasets. We use the popular synthetic-2-real domain adaptation set-ups, e.g., GTA5 Cityscapes and SYNTHIA Cityscapes. Cityscapes (Cordts et al. 2016) is a real-world dataset... GTA5 (Richter et al. 2016)... SYNTHIA (Ros et al. 2016)... |
| Dataset Splits | Yes | Training set of 2975 images is involved in the training phase. In both set-ups, 500 images of Cityscapes validation set are employed to evaluation. |
| Hardware Specification | No | The paper does not provide specific hardware details such as GPU or CPU models used for the experiments. |
| Software Dependencies | No | The paper mentions software components like "SGD optimizer", "Adam optimizer", "Deep Labv2", "VGG16", "Res Net101", "DCGAN", and "ASPP" but does not provide specific version numbers for these or other software dependencies. |
| Experiment Setup | Yes | SGD optimizer with learning rate 0.00025, momentum 0.9 and weight decay 0.0001. Domain discriminators are trained by Adam optimizer with learning rate 0.0001. As for weight transfer module, we use SGD optimizer with learning rate 0.0001, momentum 0.9 and weight decay 0. For hyper-parameters in Eq. (10), We set λ1 adv=0.0002, λ2 adv=0.001, λ1 seg=0.1, and λ2 seg=1.0 following Adapt Seg Net. We also set λ1 adv wtm = 0.0002 and λ2 adv wtm = 0.001. |