Confidence-based Visual Dispersal for Few-shot Unsupervised Domain Adaptation (C-VisDiT)

Pytorch implementation of C-VisDiT (Confidence-based Visual Dispersal Transfer)

Overview

We present the Confidence-based Visual Dispersal Transfer learning method for Few-shot Unsupervised Domain Adaptation, aiming to comprehensively consider the importance of each sample during transfer based on its confidence.

Compared with state-of-the-art methods, PCS improves the mean classification accuracy over different domain pairs on FUDA by 4.4%/1.7% (1-shot/3-shots labeled source), 2.6%/2.8% (3%/6% labeled source), 1.5% (1% labeled source), and 2.0%/2.5% (1-shot/3-shots labeled source) on Office-31, Office-Home, VisDA-C, and DomainNet, respectively.

Requirements

conda create -n cvisdit python=3.7.11
conda activate cvisdit

conda install pytorch==1.8.0 torchvision==0.9.0 torchaudio==0.8.0 cudatoolkit=11.1 -c pytorch -c conda-forge
pip install dotmap faiss-gpu==1.7.0 scikit-learn tensorboard tqdm

Training

• Download the datasets from the Internet (Split files are provided in data/splits, we now support Office, Office-Home, VisDA-2017, and DomainNet)
• Soft-link the datasets under the data folder (alias for corresponding datasets are office, officehome, visda17, and domainnet)
• To train the model, please refer to train.sh. We provide training configurations and SOTA result training-logs in the ./config and the ./logs folders. Please note that model performance is not sensitive to hyper parameters added by C-VisDiT (confidence_params). These hyper parameters can change in a wide range without greatly affecting the final results.

Citation

@inproceedings{xiong2023confidence,
  title={Confidence-based Visual Dispersal for Few-shot Unsupervised Domain Adaptation},
  author={Xiong, Yizhe and Chen, Hui and Lin, Zijia and Zhao, Sicheng and Ding, Guiguang},
  booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision},
  pages={11621--11631},
  year={2023}
}

Acknowlegdement

This code is built on [PCS]. We thank the authors for sharing their code and some of the training configuration files. We reproduced some of the PCS results on our own.

ToDo's

• Upload training scripts for VisDA-C and BrAD-based DomainNet.