Mapping Conditional Distributions for Domain Adaptation Under Generalized Target Shift

This repository contains the official code of OSTAR in “Mapping Conditional Distributions for Domain Adaptation Under Generalized Target Shift” (ICLR 2022).

Quickstart

• Install the requirements pip install -r requirements.txt
• Run training. ex: python run.py -t 000000000001 -d digits -i 1 -g 0 -s 10
• Results are logged in ./results/run_id where run_id is the id of the run.

Options

python run.py [-t MODEL] [-d DATASET] [-i RUN_ITERATIONS] [-g GPUID] [-s SETTING]

• Choose the model (see Section 5 of the paper for more details):
  • -t 100000000000: Source
  • -t 010000000000: DANN
  • -t 001000000000: WD_beta for beta = 0
  • -t 000111100000: WD_beta for beta in {1, 2, 3, 4}
  • -t 000000011000: MARSg / MARSc
  • -t 000000000100: IW-WD
  • -t 000000000010: WD_gt with true class-rations
  • -t 000000000001: OSTAR
• Choose the dataset:
  • -d digits: Digits
  • -d office: Office31 and OfficeHome. Requires downloading pre-computed features at https://github.com/jindongwang/transferlearning/blob/master/data/dataset.md
  • -d visda: VisDA12. Requires downloading pre-computed features at http://csr.bu.edu/ftp/visda17/clf/ and preprocessing downloaded file with prepare_data_visda12.py
• Choose the number of runs (e.g. 1 for a single run)
• Choose the gpu id (e.g. 0)
• Choose the label shift setting defined in compare_digits_setting.py, compare_office_setting.py, compare_visda_setting.py

Citation

@inproceedings{Kirchmeyer2022,
title={Mapping conditional distributions for domain adaptation under generalized target shift},
author={Matthieu Kirchmeyer and Alain Rakotomamonjy and Emmanuel de Bezenac and patrick gallinari},
booktitle={International Conference on Learning Representations},
year={2022},
url={https://openreview.net/forum?id=sPfB2PI87BZ}
}

GitHub

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