An Overview of Transfer Learning

Transfer learning has attracted increasingly attention in artificial intelligence, machine learning and many other application areas. Different from traditional machine learning methods which assume the training and testing data come from the same task or domain, transfer learning aims to extract common knowledge across domains or tasks, such that a model trained on one domain or task can be adapted to other domains or tasks. In this talk, I will first give an overview of transfer learning and discuss the relationships between transfer learning and other learning areas, and then summarize various transfer learning approaches into several categories, and introduce some representative methods, finally discuss research challenges and future directions in this area.