/ETN

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

Primary LanguagePython

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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