On Balancing Bias and Variance in Unsupervised Multi-Source-Free Domain Adaptation

This repository contains the code for implementing On Balancing Bias and Variance in Unsupervised Multi-Source-Free Domain Adaptation.

Requirements

• python == 3.8.8
• pytorch == 1.10.0
• torchvision == 0.11.1
• numpy, scipy, sklearn, argparse, tqdm, PIL

Datasets

Office

• Please manually download the three datasets: Office, Office-Caltech, and Office-Home.
• Please create a directory named "data", and move gen_list.py inside the directory.
• To generate the image list file for each office dataset,

python ./data/gen_list.py

Usage

• Take one dataset [Office] as an example.
• To train the source models,

python train_source.py --dset office --s 0 --max_epoch 100 --trte val --gpu_id 0 --output ckps/source/

• Please complete the training of all source models before starting domain adaptation.
• To adapt the source models to target,

python adapt.py --dset office --t 0 --max_iterations 20 --gpu_id 0 --output_src ckps/source/

Reference

The implementation is based on this repo: DECISION.

Citation

If this code is helpful for your research, please consider citing our paper.

@inproceedings{shen2023balancing,
  title={On Balancing Bias and Variance in Unsupervised Multi-Source-Free Domain Adaptation},
  author={Shen, Maohao and Bu, Yuheng and Wornell, Gregory W},
  booktitle={International Conference on Machine Learning},
  pages={30976--30991},
  year={2023},
  organization={PMLR}
}