Dirichlet-based Uncertainty Calibration for Active Domain Adaptation [ICLR 2023 Spotlight]

by Mixue Xie, Shuang Li, Rui Zhang and Chi Harold Liu

  

• Overview
• Prerequisites Installation
• Datasets Preparation
• Code Running
• Acknowledgments
• Citation
• Contact

Overview

We propose a Dirichlet-based Uncertainty Calibration (DUC) approach for active domain adaptation (DA). It provides a novel perspective for active DA by introducing the Dirichlet-based evidential model and designing an uncertainty origin-aware selection strategy to comprehensively evaluate the value of samples.

Prerequisites Installation

• For cross-domain image classification tasks, this code is implemented with Python 3.7.5, CUDA 11.4 on NVIDIA GeForce RTX 2080 Ti. To try out this project, it is recommended to set up a virtual environment first.

  # Step-by-step installation
  conda create --name DUC_cls python=3.7.5
  conda activate DUC_cls
  
  # this installs the right pip and dependencies for the fresh python
  conda install -y ipython pip
  
  # this installs required packages
  pip install -r requirements_cls.txt

• For cross-domain semantic segmentation tasks, this code is implemented with Python 3.7.5, CUDA 11.2 on NVIDIA GeForce RTX 3090. To try out this project, it is recommended to set up a virtual environment first.

  # Step-by-step installation
  conda create --name DUC_seg python=3.7.5
  conda activate DUC_seg
  
  # this installs the right pip and dependencies for the fresh python
  conda install -y ipython pip
  
  # this installs required packages
  python3 -m pip install torch==1.7.0+cu110 torchvision==0.8.1+cu110 torchaudio===0.7.0 -f https://download.pytorch.org/whl/torch_stable.html
  pip install -r requirements_seg.txt

Datasets Preparation

• Image Classification Datasets

  • Download Office-Home Dataset
  • Download VisDA-2017 Dataset
  • Download DomainNet dataset and miniDomainNet's split files

  Symlink the required dataset by running

  ln -s /path_to_home_dataset data/home
  ln -s /path_to_visda2017_dataset data/visda2017
  ln -s /path_to_domainnet_dataset data/domainnet

  The data folder should be structured as follows:

  ├── data/
  │   ├── home/     
  |   |   ├── Art/
  |   |   ├── Clipart/
  |   |   ├── Product/
  |   |   ├── RealWorld/
  │   ├── visda2017/
  |   |   ├── train/
  |   |   ├── validation/
  │   ├── domainnet/	
  |   |   ├── clipart/
  |   |   |—— infograph/
  |   |   ├── painting/
  |   |   |—— quickdraw/
  |   |   ├── real/	
  |   |   ├── sketch/	
  |   |——
  

• Semantic Segmentation Datasets

  • Download Cityscapes Dataset
  • Download GTAV Dataset
  • Download SYNTHIA Dataset

  Symlink the required dataset by running

  ln -s /path_to_cityscapes_dataset datasets/cityscapes
  ln -s /path_to_gtav_dataset datasets/gtav
  ln -s /path_to_synthia_dataset datasets/synthia

  Generate the label static files for GTAV/SYNTHIA Datasets by running

  python datasets/generate_gtav_label_info.py -d datasets/gtav -o datasets/gtav/
  python datasets/generate_synthia_label_info.py -d datasets/synthia -o datasets/synthia/

  The data folder should be structured as follows:

  ├── datasets/
  │   ├── cityscapes/     
  |   |   ├── gtFine/
  |   |   ├── leftImg8bit/
  │   ├── gtav/
  |   |   ├── images/
  |   |   ├── labels/
  |   |   ├── gtav_label_info.p
  │   └──	synthia
  |   |   ├── RAND_CITYSCAPES/
  |   |   ├── synthia_label_info.p
  │   └──	
  

Code Running

• for cross-domain image classification:

  # running for cross-domain image classification:
  sh script_cls.sh

• for cross-domain semantic segmentation:

  # go to the directory of semantic segmentation tasks
  cd ./segmentation
  
  # running for GTAV to Cityscapes
  sh script_seg_gtav.sh
  
  # running for SYNTHIA to Cityscapes
  sh script_seg_syn.sh

Acknowledgments

This project is based on the following open-source projects. We thank their authors for making the source code publicly available.

• Transferable-Query-Selection
• EADA
• RIPU

Citation

If you find this work helpful to your research, please consider citing the paper:

@inproceedings{xie2023DUC,
  title={Dirichlet-based Uncertainty Calibration for Active Domain Adaptation},
  author={Xie, Mixue and Li, Shuang and Zhang, Rui and Liu, Chi Harold},
  booktitle={International Conference on Learning Representations (ICLR)},
  year={2023}
}

Contact

If you have any problem about our code, feel free to contact mxxie@bit.edu.cn or describe your problem in Issues.