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Coupled Holistic Adversarial Transport Terms with Yield for Unsupervised Domain Adaptation

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PWC PWC PWC

CHATTY implemeneted 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 commands for each task. Here, wt represents the parameter for weight of the transfer loss.

Office-31

python train.py --gpu_id 0 --dset office --s_dset_path data/office/amazon_list.txt --t_dset_path data/office/dslr_list.txt --output_dir chatty/adn --wt 0.001 --domains A_to_D

Office-Home

python train.py --gpu_id 0 --dset office-home --s_dset_path data/office-home/Art.txt --t_dset_path data/office-home/Clipart.txt --output_dir chatty/ArCl --wt 0.0001 --domains Ar_to_Cl

Fhist

python train.py --gpu_id 0 --dset fhist --s_dset_path data/fhist/labeled_source.txt --t_dset_path data/fhist/unlabeled_target.txt --output_dir chatty/CrcNct --wt 0.0001 --domains CRC_to_NCT

The codes are heavily borrowed from GVB

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Coupled Holistic Adversarial Transport Terms with Yield for Unsupervised Domain Adaptation

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