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Code released for CVPR 2019 paper "Learning to Transfer Examples for Partial Domain Adaptation"

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Learning to Transfer Examples for Partial Domain Adaptation

Code release for Learning to Transfer Examples for Partial Domain Adaptation (CVPR 2019)

Requirements

  • python 3.6+
  • PyTorch 1.0

pip install -r requirements.txt

Usage

  • download datasets

  • write your config file

  • python main.py --config /path/to/your/config/yaml/file

  • train (configurations in officehome-train-config.yaml are only for officehome dataset):

    python main.py --config officehome-train-config.yaml

  • test

    python main.py --config officehome-test-config.yaml

  • monitor (tensorboard required)

    tensorboard --logdir .

Checkpoints

We provide the checkpoints for officehome datasets at Google Drive.

Citation

please cite:

@InProceedings{ETN_2019_CVPR,
author = {Zhangjie Cao, Kaichao You, Mingsheng Long, Jianmin Wang, Qiang Yang},
title = {Learning to Transfer Examples for Partial Domain Adaptation},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2019}
}

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Code released for CVPR 2019 paper "Learning to Transfer Examples for Partial Domain Adaptation"

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