Transfer Learning Toolkit: Primers and Benchmarks

The transfer learning toolkit wraps the codes of 17 transfer learn ing 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 applica tions. The toolkit is written in Python and distributed under MIT open source license. In this pape r, the current state of this toolkit is described and the necessary environment setting and usage are in troduced. Keywords: Transfer Learning, Toolkit 1. Introduction Transfer learning is a promising and important direction in machine lear ning, which attempts to leverage the knowledge contained in a source domain to improve the le arning performance or minimize the number of labeled samples required in a target domain.