A Systematic Review of Transfer Learning Research From the Perspective of Knowledge Graph: Visual Analysis Based on the Bibliometric Method

ABSTRACT As a novel paradigm to overcome the problems of limited data and model generalization in the era of big data, transfer learning (TL) methods have become an essential branch of machine learning. However, the academic community still needs a data‐driven, dynamic, and quantitatively structured review of TL. This paper visually summarizes the literature growth and associated disciplinary trends of TL topics from a knowledge mapping perspective using 4496 documents collected in the WOS core collection, combined with bibliometric. The most essential 30 TL research results were identified and mapped spatial–temporally. This was followed by a structured analysis of the most influential research vectors including countries, institutions, authors, and publication sources. Following this, the knowledge base, core topic distribution, hot topics, and knowledge evolution process of TL research are systematically analyzed around the construction of a co‐cited literature network and keyword co‐occurrence network. The challenges of TL research are thereby summarized and future directions are proposed. This study provides researchers in the area of TL with a holistic insight into research trends, as well as conclusive and comprehensive analysis results that may help relevant scholars better grasp its dynamic direction. This article is categorized under: Technologies > Machine Learning Technologies > Artificial Intelligence