PAN

Code release for "Progressive Adversarial Networks for Fine-Grained Domain Adaptation" (CVPR 2020)

Prerequisites:

• Python3
• PyTorch == 0.4.1 (with suitable CUDA and CuDNN version)
• torchvision >= 0.2.1

Dataset:

You need to modify the path of the image in every ".txt" in "./dataset_list".

The sub-dataset CUB-200-Paintings of CUB-Paintings is provided in the following Google Drive links: https://drive.google.com/file/d/1G327KsD93eyGTjMmByuVy9sk4tlEOyK3/view?usp=sharing

Training on one dataset:

You can use the following commands to the tasks:

python PAN.py --gpu_id n --source c --target p

Citation:

If you use this code for your research, please consider citing:

@inproceedings{PAN_20,
  title={Progressive Adversarial Networks for Fine-Grained Domain Adaptation},  
  author={Wang, Sinan and Chen, Xinyang and Wang, Yunbo and Long, Mingsheng and Wang, Jianmin}, 
  booktitle={The IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}, 
  pages={9213-9222}, 
  year={2020} 
}

Contact

If you have any problem about our code, feel free to contact [email protected].