https://github.com/xyupeng/beta
[ICLR 2023 Spotlight] Divide to Adapt: Mitigating Confirmation Bias for Domain Adaptation of Black-Box Predictors
https://github.com/xyupeng/beta
domain-adaptation
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[ICLR 2023 Spotlight] Divide to Adapt: Mitigating Confirmation Bias for Domain Adaptation of Black-Box Predictors
- Host: GitHub
- URL: https://github.com/xyupeng/beta
- Owner: xyupeng
- Created: 2022-05-28T06:57:32.000Z (over 3 years ago)
- Default Branch: main
- Last Pushed: 2023-07-07T06:11:22.000Z (over 2 years ago)
- Last Synced: 2024-11-15T05:32:48.191Z (11 months ago)
- Topics: domain-adaptation
- Language: Python
- Homepage:
- Size: 1.44 MB
- Stars: 39
- Watchers: 3
- Forks: 5
- Open Issues: 2
-
Metadata Files:
- Readme: README.md
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README
# 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"](https://openreview.net/pdf?id=hVrXUps3LFA).
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](https://github.com/jindongwang/transferlearning/blob/master/data/dataset.md)
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).
