Transfer Learning Toolkit: Primers and Benchmarks
This toolkit addresses the problem of accessibility and ease of use for primary researchers in applying transfer learning models, but it is incremental as it packages existing methods without new algorithmic contributions.
The authors introduced a Python toolkit that wraps 17 transfer learning models with integrated interfaces to simplify usage for researchers and real-world applications, but no concrete results or performance numbers were reported.
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.