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CDAN-GD

README.md

CDAN-GD implemneted in PyTorch

Prerequisites

  • pytorch = 1.0.1
  • torchvision = 0.2.1
  • numpy = 1.17.2
  • pillow = 6.2.0
  • python3.6
  • cuda10

Training

The following are the command for each task. The GVBG and GVBD represents the parameter for GVB on the generator and discriminator. if GVBG==0, GVBG is not utilized for the network. The test_interval can be changed, which is the number of iterations between near test. The num_iterations can be changed, which is the total training iteration number.

Office-31

python train_image.py CDAN+E --gpu_id 0 --GVBG 1 --GVBD 1 --num_iterations 8004  --dset office --s_dset_path data/office/amazon_list.txt --t_dset_path data/office/dslr_list.txt --test_interval 500 --output_dir gvbgd/adn

Office-Home

python train_image.py CDAN+E --gpu_id 0 --GVBG 1 --GVBD 1 --num_iterations 8004  --dset office-home --s_dset_path data/office-home/Art.txt --t_dset_path data/office-home/Clipart.txt --test_interval 500 --output_dir gvbgd/ArCl

VisDA 2017

python train_image.py CDAN+E --gpu_id 0 --GVBG 1 --GVBD 1 --num_iterations 15002 --dset visda --s_dset_path data/visda-2017/train_list.txt --t_dset_path data/visda-2017/validation_list.txt --test_interval 1000  --output_dir gvbgd

The codes are heavily borrowed from CDAN