Prototypical Cross-domain Self-supervised Learning for Few-shot Unsupervised Domain Adaptation (PCS)

Pytorch implementation of PCS (Prototypical Cross-domain Self-supervised network) [Homepage] [PDF]

Overview

Architecture of Network

Compared with state-of-the-art methods, PCS improves the mean classification accuracy over different domain pairs on FUDA by 10.5%, 4.3%, 9.0%, and 13.2% on Office, Office-Home, VisDA-2017, and DomainNet, respectively. q

Requirements

conda install pytorch==1.5.1 torchvision==0.6.1 cudatoolkit=10.2 -c pytorch
pip install -r requirements.txt
pip install -e .

Training

• Download or soft-link your dataset under data folder (Split files are provided in data/splits, supported datasets are Office, Office-Home, VisDA-2017, and DomainNet)
• To train the model, run following commands:

CUDA_VISIBLE_DEVICES=0 python pcs/run.py --config config/${DATASET}/${DOMAIN-PAIR}.json
CUDA_VISIBLE_DEVICES=0,1 python pcs/run.py --config config/office/D-A-1.json

[2021.06.24] We released all configs for office dataset.

Citation

@InProceedings{Yue_2021_Prototypical,
author = {Yue, Xiangyu and Zheng, Zangwei and Zhang, Shanghang and Gao, Yang and Darrell, Trevor and Keutzer, Kurt and Sangiovanni-Vincentelli, Alberto},
title = {Prototypical Cross-domain Self-supervised Learning for Few-shot Unsupervised Domain Adaptation},
booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2021}
}

Acknowlegdement

This code is built on [MME]. We thank the authors for sharing their codes.