Transfer Learning Toolkit: Primers and Benchmarks

2019/11/20 by Fuzhen Zhuang, Keyu Duan, Zhuang, Fuzhen +11
Computer Science · #Domain Adaptation and Few-Shot Learning #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Machine Learning and Algorithms #Topic Modeling

paper · pdf · doi:10.48550/arxiv.1911.08967

openalex publication_date 2019/11/20 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

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.

Citations

• Marginalized Denoising Autoencoders for Domain Adaptation
• Learning Transferable Features with Deep Adaptation Networks
• A Survey on Transfer Learning
• A Comprehensive Survey on Transfer Learning

Related

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• A Comprehensive Survey on Transfer Learning
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• Research Frontiers in Transfer Learning -- a systematic and bibliometric review
• Visual Transfer Learning: Informal Introduction and Literature Overview