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.

Environment

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

Datasets

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 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

Easy-hard domain division

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