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}
}
- Install
pytorchandtorchvision(we usepytorch==1.9.1andtorchvision==0.10.1). pip install -r requirements.txt
Please download and organize the datasets in this structure:
BETA
├── data
├── office_home
│ ├── Art
│ ├── Clipart
│ ├── Product
│ ├── Real World
├── office31
│ ├── amazon
│ ├── dslr
│ ├── webcam
├── visda17
│ ├── train
│ ├── validation
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 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 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 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
Here we show an example of the easy-hard target domain division (Office-Home: Art -> Clipart).
React
Typescript
Django
Laravel
Facebook
Microsoft
Google
Alibaba
D3
Tencent