partial_domain_adaptation

Partial Domain Adaptation for Image Classification

Installation

Example scripts also support all models in PyTorch-Image-Models. You also need to install timm to use PyTorch-Image-Models.

pip install timm

Dataset

Following datasets can be downloaded automatically:

• Office31
• OfficeHome
• VisDA2017

You need to prepare following datasets manually if you want to use them:

• ImageNet

and prepare them following Documentation for ImageNetCaltech and CaltechImageNet.

Supported Methods

Supported methods include:

• Domain Adversarial Neural Network (DANN)
• Partial Adversarial Domain Adaptation (PADA)
• Importance Weighted Adversarial Nets (IWAN)
• Adaptive Feature Norm (AFN)

Experiment and Results

The shell files give the script to reproduce the benchmarks with specified hyper-parameters. For example, if you want to train DANN on Office31, use the following script

# Train a DANN on Office-31 Amazon -> Webcam task using ResNet 50.
# Assume you have put the datasets under the path `data/office-31`, 
# or you are glad to download the datasets automatically from the Internet to this path
CUDA_VISIBLE_DEVICES=0 python dann.py data/office31 -d Office31 -s A -t W -a resnet50 --epochs 20 --seed 1 --log logs/dann/Office31_A2W

For more information please refer to Get Started for help.

Citation

If you use these methods in your research, please consider citing.

@inproceedings{DANN,
    author = {Ganin, Yaroslav and Lempitsky, Victor},
    Booktitle = {ICML},
    Title = {Unsupervised domain adaptation by backpropagation},
    Year = {2015}
}

@InProceedings{PADA,
    author    = {Zhangjie Cao and
               Lijia Ma and
               Mingsheng Long and
               Jianmin Wang},
    title     = {Partial Adversarial Domain Adaptation},
    booktitle = {ECCV},
    year = {2018}
}

@InProceedings{IWAN,
    author    = {Jing Zhang and
               Zewei Ding and
               Wanqing Li and
               Philip Ogunbona},
    title     = {Importance Weighted Adversarial Nets for Partial Domain Adaptation},
    booktitle = {CVPR},
    year = {2018}
}

@InProceedings{AFN,
    author = {Xu, Ruijia and Li, Guanbin and Yang, Jihan and Lin, Liang},
    title = {Larger Norm More Transferable: An Adaptive Feature Norm Approach for Unsupervised Domain Adaptation},
    booktitle = {ICCV},
    year = {2019}
}