Divide to Adapt: Mitigating Confirmation Bias for Domain Adaptation of Black-Box Predictors

This repo is the official implementation of "Divide to Adapt: Mitigating Confirmation Bias for Domain Adaptation of Black-Box Predictors". Our method is termed as BETA. To cite this work:

@article{yang2022divide,
  title={Divide to Adapt: Mitigating Confirmation Bias for Domain Adaptation of Black-Box Predictors},
  author={Yang, Jianfei and Peng, Xiangyu and Wang, Kai and Zhu, Zheng and Feng, Jiashi and Xie, Lihua and You, Yang},
  journal={arXiv preprint arXiv:2205.14467},
  year={2022}
}

News

2023-01-21: The paper was accepted by ICLR 2023 as spotlight (notable-top-25%)!

Environment

1. Install pytorch and torchvision (we use pytorch==1.9.1 and torchvision==0.10.1).
2. pip install -r requirements.txt

Datasets

Please download and organize the datasets as the following structure, where DN_domain for DomainNet is clipart/infograph/painting/quickdraw/real/sketch

BETA/
├── data/
    ├── office_home/
    │   ├── Art
    │   ├── Clipart
    │   ├── Product
    │   ├── Real World
    ├── office31/
    │   ├── amazon
    │   ├── dslr
    │   ├── webcam
    ├── visda17/
    │   ├── train
    │   ├── validation 
    ├── DomainNet/
    │   ├── ${DN_domain}/
    │   ├── ${DN_domain}_train.txt
    │   ├── ${DN_domain}_test.txt

Then generate info files with the following commands:

python dev/generate_infos.py --ds office_home
python dev/generate_infos.py --ds office31
python dev/generate_infos.py --ds visda17

Train on Office-Home

# train black-box source model on domain A
python train_src_v1.py configs/office_home/src_A/train_src_A.py

# adapt with BETA, from A to C
python train_BETA.py configs/office_home/src_A/BETA_C.py

# finetune on C
python finetune.py configs/office_home/src_A/finetune_C.py

Train on Office-31

# train black-box source model on domain a
python train_src_v1.py configs/office31/src_a/train_src_a.py

# adapt with BETA, from a to d
python train_BETA.py configs/office31/src_a/BETA_d.py

# finetune on d 
python finetune.py configs/office31/src_a/finetune_d.py

Train on VisDA-2017

# train black-box source model
python train_src_v2.py configs/visda17/train_src.py

# adapt with BETA
python train_BETA.py configs/visda17/BETA.py

Train on DomainNet

# C/I/P/Q/R/S stands for clipart/infograph/painting/quickdraw/real/sketch respectively
SRC=C
TGT=I

# train source model
python train_src_v2.py configs/DomainNet/src_${SRC}/train_src_${SRC}.py

# adapt with BETA
python train_BETA.py configs/DomainNet/src_${SRC}/BETA_${TGT}.py

Easy-hard domain division

Here we show an example of the easy-hard target domain division (Office-Home: Art -> Clipart).