A Decade Survey of Transfer Learning (2010–2020)
By Shuteng Niu, Yongxin Liu, Jian Wang, Houbing Song. IEEE Transactions on Artificial Intelligence, 2020
551 citations recorded by the source corpus.
Transfer learning (TL) has been successfully applied to many real-world problems that traditional machine learning (ML) cannot handle, such as image processing, speech recognition, and natural language processing (NLP). Commonly, TL tends to address three main problems of traditional machine learning: (1) insufficient labeled data, (2) incompatible computation power, and (3) distribution mismatch. In general, TL can be organized into four categories: transductive learning, inductive learning, unsupervised learning, and negative learning. Furthermore, each category can be organized into four learning types: learning on instances, learning on features, learning on parameters, and learning on relations. This article presents a comprehensive survey on TL. In addition, this article presents the state of the art, current trends, applications, and open challenges.
Subfields: Artificial Intelligence
View Paper: https://www.semanticscholar.org/paper/10827fb697b6f426b0d6f4905053aafa51a8786f
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In Artificial Intelligence
Citations in the corpus
- Learning Bounds for Open-Set Learning (2021, methodology): “Based on this fact, we aim to establish the OSL theory from transfer learning (Dong et al., 2019; 2020a;b; 2021; Liu et al., 2019; Lu et al., 2015; Luo et al., 2020a; Niu et al., 2020; Pan & Yang, 2010; Wang et al., 2020), which learns knowledge for a given domain from a different, but relative doma”
- Developing clinical prediction models: a step-by-step guide (2024, unknown): “31 Further guidance on external validation and model updating is available elsewhere, 32-36 including sample size considerations for external validation.”