The transfer learning toolkit wraps the codes of 17 transfer learning models and provides integrated interfaces, allowing users to use those models by calling a simple function. It is easy for primary researchers to use this toolkit and to choose proper models for real-world applications. The toolkit is written in Python and distributed under MIT open source license. In this paper, the current state of this toolkit is described and the necessary environment setting and usage are introduced.
@article{arxiv.1911.08967,
title = {Transfer Learning Toolkit: Primers and Benchmarks},
author = {Fuzhen Zhuang and Keyu Duan and Tongjia Guo and Yongchun Zhu and Dongbo Xi and Zhiyuan Qi and Qing He},
journal= {arXiv preprint arXiv:1911.08967},
year = {2019}
}