OneRing: A Simple Method for Source-free Open-partial Domain Adaptation

Shiqi Yang, Yaxing Wang, Kai Wang, Shangling Jui and Joost van de Weijer

Keyworkds: Open-set Recognition; Open-set Single Domain Generalization; Source-free Universal/Open-partial Domain Adaptation

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Code for our paper 'OneRing: A Simple Method for Source-free Open-partial Domain Adaptation'

[project][arxiv]

Demo for our OneRing classifier

Trained on 3 known categories.

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Training for open-set single domain generalization and source-free open-partial domain adaptation

(Attention: Codes are based on pytorch 1.3 with cuda 10.0, please ensure the same pytorch version for reproducing)

1. Download datasets and change the corresponding path in /data/*.txt
2. Training

• Run the following command for the whole training on Office-31 under open-set single domain generalization, the model will be only trained on source and directly evaluated on target domain.

python train_office31_ossdg.py --gpu 0

• Run the following command for the whole training on Office-Home and VisDA under source-free open-partial domain adaptation, the model will be first trained on source and then adapt to target domain without source data. (lpa refers to our AaD.)

sh officehome_sfunda.sh

python train_visda_sfunda.py --gpu 0 --save_model --lpa

(We provide the model weight after adaptation for SF-OPDA on Office-Home and VisDA, check the link here.)

You can check the old arxiv version for results of other tasks.

Reference

@article{yang2022one,
title={One Ring to Bring Them All: Towards Open-Set Recognition under Domain Shift},
author={Yang, Shiqi and Wang, Yaxing and Wang, Kai and Jui, Shangling and van de Weijer, Joost},
journal={arXiv preprint arXiv:2206.03600},
year={2022}
}