/McDA

Code for "Maximum Mean and Covariance Discrepancy for Unsupervised Domain Adaptation"

Primary LanguageMATLAB

Code for "Maximum Mean and Covariance Discrepancy for Unsupervised Domain Adaptation".

  • demo_pie.m: code for the results of McDA on the PIE dataset shown in Table 1.
  • demo_office.m: code for the results of McDA on the Office-Caltech dataset shown in Table 2.

If you find this code useful, please consider citing

@article{zhang2019maximum,
  title={Maximum Mean and Covariance Discrepancy for Unsupervised Domain Adaptation},
  author={Zhang, Wenju and Zhang, Xiang and Lan, Long and Luo, Zhigang},
  journal={Neural Processing Letters},
  pages={1--20},
  year={2019},
  publisher={Springer}
}

Note that the code framework is borrowed from "Long M, Wang J, Ding G, Sun J, Yu PS (2013) Transfer feature learning with joint distribution adaptation. In: IEEE international conference on computer vision, pp 2200–2207".

Please let me know if you have any questions about our work (Wenju Zhang, zhangwenju13@nudt.edu.cn).