DIGITS EXPERIMENTS

To download the datasets, you can e.g. unpack the following file in data/digits. For the sake of speed when running experiments, the code generates a pkl file containing the whole datasets (when first run) and then loads it at runtime.

To run the code on the original datasets, run:

python train_digits.py {method} --task {task}

where method belongs to: [CDAN, CDAN-E, DANN, IWDAN, NANN, IWDANORACLE, IWCDAN, IWCDANORACLE, IWCDAN-E, IWCDAN-EORACLE] and task is either mnist2usps or usps2mnist.

To run the code on the subsampled datasets, run:

python train_digits.py {method} --task {task} --ratio 1

To reproduce Figs. 1 and 2, run the following command with various seeds:

python train_digits.py {method} --ratio {ratio}

where ratio belongs to 100 <= ratio < 150 (to subsample the target) or to 200 <= ratio < 250 (to subsample the source). Each corresponds to a given subsampling, the exact fractions can be found in the subsampling list of `data_list.txt.

VISDA AND OFFICE DATASETS

The Visda dataset can be found here: https://github.com/VisionLearningGroup/taskcv-2017-public.

The Office-31 dataset can be found here: https://people.eecs.berkeley.edu/~jhoffman/domainadapt.

The Office-Home dataset can be found here: http://hemanthdv.org/OfficeHome-Dataset.

They should be downloaded and placed in the corresponding folder under data. The code will generate a test file once for faster evaluation, it might take a while during the first visda run.

Discriminator based methods

To run the code on the original datasets, run:

python train_image.py {method} --dset {dset} --s_dset_file {s_dset_file} --t_dset_file {t_dset_file}

where:

• method belongs to [CDAN, CDAN-E, DANN, IWDAN, NANN, IWDANORACLE, IWCDAN, IWCDANORACLE, IWCDAN-E, IWCDAN-EORACLE]
• dset belongs to [visda, office-31, office-home]
• s_dset_file corresponds to the source domain, the filename can be found in the corresponding data folder, e.g. dslr_list.txt (not needed for VISDA)
• t_dset_file corresponds to the target domain, the filename can be found in the corresponding data folder, e.g. amazon_list.txt (not needed for VISDA).

To run the code on the subsampled datasets, run the same command with --ratio 1 appended to it.

MMD-based methods

To run the MMD algorithms (e.g. IWJAN), use the same commands as above with the train_mmd.py file.

Reference

Please consider citing us if you use this code:

@inproceedings{tachet2020domain,
      title={Domain Adaptation with Conditional Distribution Matching and Generalized Label Shift},
      author={Tachet des Combes, Remi and Zhao, Han and Wang, Yu-Xiang and Gordon, Geoff},
      year={2020},
      booktitle={Advances in Neural Information Processing Systems}
}

This code is based upon the work of:

@inproceedings{long2018conditional,
  title={Conditional adversarial domain adaptation},
  author={Long, Mingsheng and Cao, Zhangjie and Wang, Jianmin and Jordan, Michael I},
  booktitle={Advances in Neural Information Processing Systems},
  pages={1645--1655},
  year={2018}
}

Contributing

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