A brief review on multi-task learning
Multi-task learning (MTL), which optimizes multiple related learning tasks at the same time, has been widely used in various applications, including natural language processing, speech recognition, computer vision, multimedia data processing, biomedical imaging, socio-biological data analysis, multi...
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| Published in | Multimedia tools and applications Vol. 77; no. 22; pp. 29705 - 29725 |
|---|---|
| Main Authors | , |
| Format | Journal Article |
| Language | English |
| Published |
New York
Springer US
01.11.2018
Springer Nature B.V |
| Subjects | |
| Online Access | Get full text |
| ISSN | 1380-7501 1573-7721 |
| DOI | 10.1007/s11042-018-6463-x |
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| Abstract | Multi-task learning (MTL), which optimizes multiple related learning tasks at the same time, has been widely used in various applications, including natural language processing, speech recognition, computer vision, multimedia data processing, biomedical imaging, socio-biological data analysis, multi-modality data analysis, etc. MTL sometimes is also referred to as joint learning, and is closely related to other machine learning subfields like multi-class learning, transfer learning, and learning with auxiliary tasks, to name a few. In this paper, we provide a brief review on this topic, discuss the motivation behind this machine learning method, compare various MTL algorithms, review MTL methods for incomplete data, and discuss its application in deep learning. We aim to provide the readers with a simple way to understand MTL without too many complicated equations, and to help the readers to apply MTL in their applications. |
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| AbstractList | Multi-task learning (MTL), which optimizes multiple related learning tasks at the same time, has been widely used in various applications, including natural language processing, speech recognition, computer vision, multimedia data processing, biomedical imaging, socio-biological data analysis, multi-modality data analysis, etc. MTL sometimes is also referred to as joint learning, and is closely related to other machine learning subfields like multi-class learning, transfer learning, and learning with auxiliary tasks, to name a few. In this paper, we provide a brief review on this topic, discuss the motivation behind this machine learning method, compare various MTL algorithms, review MTL methods for incomplete data, and discuss its application in deep learning. We aim to provide the readers with a simple way to understand MTL without too many complicated equations, and to help the readers to apply MTL in their applications. |
| Author | Thung, Kim-Han Wee, Chong-Yaw |
| Author_xml | – sequence: 1 givenname: Kim-Han orcidid: 0000-0003-1379-2185 surname: Thung fullname: Thung, Kim-Han organization: Department of Radiology, University of North Carolina – sequence: 2 givenname: Chong-Yaw surname: Wee fullname: Wee, Chong-Yaw email: cywee2000@gmail.com organization: Department of Biomedical Engineering, National University of Singapore |
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| Keywords | Joint learning MTL Transfer learning Learning with auxiliary tasks Multi-task learning Multi-class learning |
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