semantic_segmentation

Unsupervised Domain Adaptation for Semantic Segmentation

Dataset

You need to prepare following datasets manually if you want to use them:

• Cityscapes
• GTA5
• Synthia

and prepare them following Documentations for Cityscapes, GTA5 and Synthia,

Supported Methods

Supported methods include:

• Cycle-Consistent Adversarial Networks (CycleGAN)
• CyCADA: Cycle-Consistent Adversarial Domain Adaptation
• Adversarial Entropy Minimization (ADVENT)
• Fourier Domain Adaptation (FDA)

Experiment and Results

The shell files give the script to reproduce the benchmarks with specified hyper-parameters. For example, if you want to train ADVENT on Office31, use the following script

# Train a ADVENT on GTA5 to Cityscapes.
# Assume you have put the datasets under the path `data/GTA5` and `data/Cityscapes`, 
CUDA_VISIBLE_DEVICES=0 python advent.py data/GTA5 data/Cityscapes -s GTA5 -t Cityscapes \
    --log logs/advent/gtav2cityscapes

For more information please refer to Get Started for help.

TODO

Support methods: AdaptSeg

Citation

If you use these methods in your research, please consider citing.

@inproceedings{CycleGAN,
    title={Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks},
    author={Zhu, Jun-Yan and Park, Taesung and Isola, Phillip and Efros, Alexei A},
    booktitle={ICCV},
    year={2017}
}

@inproceedings{cycada,
    title={Cycada: Cycle-consistent adversarial domain adaptation},
    author={Hoffman, Judy and Tzeng, Eric and Park, Taesung and Zhu, Jun-Yan and Isola, Phillip and Saenko, Kate and Efros, Alexei and Darrell, Trevor},
    booktitle={ICML},
    year={2018},
}

@inproceedings{Advent,
    author = {Vu, Tuan-Hung and Jain, Himalaya and Bucher, Maxime and Cord, Matthieu and Perez, Patrick},
    title = {ADVENT: Adversarial Entropy Minimization for Domain Adaptation in Semantic Segmentation},
    booktitle = {CVPR},
    year = {2019}
}

@inproceedings{FDA,
    author    = {Yanchao Yang and
               Stefano Soatto},
    title     = {{FDA:} Fourier Domain Adaptation for Semantic Segmentation},
    booktitle = {CVPR},
    year = {2020}
}