A Survey on Deep Transfer Learning | RDL Research Database

Abstract

As a new classification platform, deep learning has recently received increasing attention from researchers and has been successfully applied to many domains. In some domains, like bioinformatics and robotics, it is very difficult to construct a large-scale well-annotated dataset due to the expense of data acquisition and costly annotation, which limits its development. Transfer learning relaxes the hypothesis that the training data must be independent and identically distributed (i.i.d.) with the test data, which motivates us to use transfer learning to solve the problem of insufficient training data. This survey focuses on reviewing the current researches of transfer learning by using deep neural network and its applications. We defined deep transfer learning, category and review the recent research works based on the techniques used in deep transfer learning.

Keywords

Computer scienceTransfer of learningArtificial intelligenceDeep learningAnnotationMachine learningConstruct (python library)Deep neural networksArtificial neural network

Affiliated Institutions

Tsinghua University CN

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Publication Info

Year: 2018
Type: book-chapter
Pages: 270-279
Citations: 2764
Access: Closed

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APA Style

                            
                                    Chuanqi Tan, 
                                
                                    Fuchun Sun, 
                                
                                    Tao Kong
                                
                                et al.
                            
                            (2018). 
                            A Survey on Deep Transfer Learning. 
                            Lecture notes in computer science
                            
                            , 270-279.
                            https://doi.org/10.1007/978-3-030-01424-7_27

Identifiers

DOI: 10.1007/978-3-030-01424-7_27
PMID: 40918614
PMCID: PMC12412780
arXiv: 1808.01974

Data Quality

Data completeness: 79%