Read the original document by opening this link in a new tab.
Table of Contents
1 Introduction
2 Related Work
3 Overview
3.1 Notation
3.2 Definition
3.3 Categorization of Transfer Learning
4 Transfer Learning Problem Categorization Solution Categorization
5 Figure 2. Categorizations of transfer learning.
Summary
Transfer learning aims at improving the performance of target learners on target domains by transferring the knowledge contained in different but related source domains. This survey attempts to connect and systematize existing transfer learning research, summarizing and interpreting mechanisms and strategies of transfer learning. It introduces over forty representative transfer learning approaches, especially homogeneous transfer learning approaches, from the perspectives of data and model. The survey focuses on the importance of selecting appropriate transfer learning models for different applications, demonstrating their performance on various datasets. The survey also discusses the connections and differences between transfer learning and related machine learning techniques.