This repository contains the official code of OSTAR in "Mapping Conditional Distributions for Domain Adaptation Under Generalized Target Shift" (ICLR 2022).
- 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.
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:https://csr.bu.edu/ftp/visda17/clf/ and preprocessing downloaded file withprepare_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
@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}
}