Transfer learning and its application research
Yancong Zhou, Xuemei Zhang, Yan Wang, Bo Zhang
Abstract
Abstract Transfer learning relaxes the assumption’s limitation requirements of the independent and identical distribution of training data and test data in machine learning. It aims to help the target domain complete the learning task by learning one or more source domains similar to the target domain, solve the problem of scarcity of annotation data and enhance the model’s robustness and generalization performance. The article is a survey on the progress of transfer learning. According to “how to transfer”, transfer learning is divided into four categories: instance-based transfer learning, feature-based transfer learning, model-based transfer learning, and relation-based transfer learning. The paper introduces the basic assumptions, main research questions, common methods, related research of various transfer learning algorithms, and the application of the transfer learning. Finally, we try to point out future research trends.