DSBF

Introduction

This repository contains the implementation code for paper:

Domain-Specific Bias Filtering for Single Labeled Domain Generalization

Junkun Yuan, Xu Ma, Defang Chen, Kun Kuang, Fei Wu, Lanfen Lin

arXiv preprint, 2021

[arXiv]

Brief Abstract for the Paper

Domain generalization (DG) utilizes multiple labeled source datasets to train a generalizable model for unseen target domains. However, due to expensive annotation costs, the requirements of labeling all the source data are hard to be met in real-world applications.

We investigate a Single Labeled Domain Generalization (SLDG) task with only one source domain being labeled, which is more practical and challenging than the Conventional Domain Generalization (CDG). A major obstacle in the SLDG task is the discriminability-generalization bias: discriminative information in the labeled source dataset may contain domain-specific bias, constraining the generalization of the trained model.

To tackle this challenging task, we propose Domain-Specific Bias Filtering (DSBF), which initializes a discriminative model with the labeled source data and filters out its domain-specific bias with the unlabeled source data for generalization improvement. We divide the filtering process into: (1) Feature extractor debiasing using k-means clustering-based semantic feature re-extraction; and (2) Classifier calibrating using attention-guided semantic feature projection.

Requirements

You may need to build suitable Python environment by installing the following packages (Anaconda is recommended).

  • python 3.6
  • pytorch 1.7.1 (with cuda 11.0 and cudnn 8.0)
  • torchvision 0.8.2
  • tensorboardx 2.1
  • numpy 1.19

Data Preparation

We list the adopted datasets in the following.

Datasets Download link
PACS [1] https://dali-dl.github.io/project_iccv2017.html
Office-Home [2] https://www.hemanthdv.org/officeHomeDataset.html

Please note:

  • Although these datasets are open-sourced, you may need to have permission to use the datasets under the datasets’ license.
  • If you’re a dataset owner and do not want your dataset to be included here, please get in touch with us via a GitHub issue. Thanks!

Usage

  1. Prepare the datasets.
  2. Update the .txt files under folder “DSBF/dataset/pthList/” with your dataset path.
  3. Run the code with command:
run.txt 2>&1 &”>

nohup sh run.sh > run.txt 2>&1 &
  1. Check results in DSBF/dataset–task–target-data.txt.

Citation

If you find our code or idea useful for your research, please cite our work.

@article{yuan2021domain,
  title={Domain-Specific Bias Filtering for Single Labeled Domain Generalization},
  author={Yuan, Junkun and Ma, Xu and Chen, Defang and Kuang, Kun and Wu, Fei and Lin, Lanfen},
  journal={arXiv preprint arXiv:2110.00726},
  year={2021}
}

Contact

If you have any questions, feel free to contact us through email () or GitHub issues. Thanks!

References

[1] Li, Da, et al. “Deeper, broader and artier domain generalization.” Proceedings of the IEEE international conference on computer vision. 2017.

[2] Venkateswara, Hemanth, et al. “Deep hashing network for unsupervised domain adaptation.” Proceedings of the IEEE conference on computer vision and pattern recognition. 2017.

GitHub

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