Self-supervised Learning: A Succinct Review
Machine learning has made significant advances in the field of image processing. The foundation of this success is supervised learning, which necessitates annotated labels generated by humans and hence learns from labelled data, whereas unsupervised learning learns from unlabeled data. Self-supervis...
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| Veröffentlicht in: | Archives of computational methods in engineering Jg. 30; H. 4; S. 2761 - 2775 |
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Springer Netherlands
01.05.2023
Springer Nature B.V |
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| ISSN: | 1134-3060, 1886-1784, 1886-1784 |
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| Abstract | Machine learning has made significant advances in the field of image processing. The foundation of this success is supervised learning, which necessitates annotated labels generated by humans and hence learns from labelled data, whereas unsupervised learning learns from unlabeled data. Self-supervised learning (SSL) is a type of un-supervised learning that helps in the performance of downstream computer vision tasks such as object detection, image comprehension, image segmentation, and so on. It can develop generic artificial intelligence systems at a low cost using unstructured and unlabeled data. The authors of this review article have presented detailed literature on self-supervised learning as well as its applications in different domains. The primary goal of this review article is to demonstrate how images learn from their visual features using self-supervised approaches. The authors have also discussed various terms used in self-supervised learning as well as different types of learning, such as contrastive learning, transfer learning, and so on. This review article describes in detail the pipeline of self-supervised learning, including its two main phases: pretext and downstream tasks. The authors have shed light on various challenges encountered while working on self-supervised learning at the end of the article. |
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| AbstractList | Machine learning has made significant advances in the field of image processing. The foundation of this success is supervised learning, which necessitates annotated labels generated by humans and hence learns from labelled data, whereas unsupervised learning learns from unlabeled data. Self-supervised learning (SSL) is a type of un-supervised learning that helps in the performance of downstream computer vision tasks such as object detection, image comprehension, image segmentation, and so on. It can develop generic artificial intelligence systems at a low cost using unstructured and unlabeled data. The authors of this review article have presented detailed literature on self-supervised learning as well as its applications in different domains. The primary goal of this review article is to demonstrate how images learn from their visual features using self-supervised approaches. The authors have also discussed various terms used in self-supervised learning as well as different types of learning, such as contrastive learning, transfer learning, and so on. This review article describes in detail the pipeline of self-supervised learning, including its two main phases: pretext and downstream tasks. The authors have shed light on various challenges encountered while working on self-supervised learning at the end of the article. Machine learning has made significant advances in the field of image processing. The foundation of this success is supervised learning, which necessitates annotated labels generated by humans and hence learns from labelled data, whereas unsupervised learning learns from unlabeled data. Self-supervised learning (SSL) is a type of un-supervised learning that helps in the performance of downstream computer vision tasks such as object detection, image comprehension, image segmentation, and so on. It can develop generic artificial intelligence systems at a low cost using unstructured and unlabeled data. The authors of this review article have presented detailed literature on self-supervised learning as well as its applications in different domains. The primary goal of this review article is to demonstrate how images learn from their visual features using self-supervised approaches. The authors have also discussed various terms used in self-supervised learning as well as different types of learning, such as contrastive learning, transfer learning, and so on. This review article describes in detail the pipeline of self-supervised learning, including its two main phases: pretext and downstream tasks. The authors have shed light on various challenges encountered while working on self-supervised learning at the end of the article.Machine learning has made significant advances in the field of image processing. The foundation of this success is supervised learning, which necessitates annotated labels generated by humans and hence learns from labelled data, whereas unsupervised learning learns from unlabeled data. Self-supervised learning (SSL) is a type of un-supervised learning that helps in the performance of downstream computer vision tasks such as object detection, image comprehension, image segmentation, and so on. It can develop generic artificial intelligence systems at a low cost using unstructured and unlabeled data. The authors of this review article have presented detailed literature on self-supervised learning as well as its applications in different domains. The primary goal of this review article is to demonstrate how images learn from their visual features using self-supervised approaches. The authors have also discussed various terms used in self-supervised learning as well as different types of learning, such as contrastive learning, transfer learning, and so on. This review article describes in detail the pipeline of self-supervised learning, including its two main phases: pretext and downstream tasks. The authors have shed light on various challenges encountered while working on self-supervised learning at the end of the article. |
| Author | Kumar, Krishan Mittal, Ajay Kumar, Munish Nabi, Syed Tufael Rani, Veenu |
| Author_xml | – sequence: 1 givenname: Veenu surname: Rani fullname: Rani, Veenu organization: Department of Computational Sciences, Maharaja Ranjit Singh Punjab Technical University – sequence: 2 givenname: Syed Tufael surname: Nabi fullname: Nabi, Syed Tufael organization: Department of Computational Sciences, Maharaja Ranjit Singh Punjab Technical University – sequence: 3 givenname: Munish orcidid: 0000-0003-0115-1620 surname: Kumar fullname: Kumar, Munish email: munishcse@gmail.com organization: Department of Computational Sciences, Maharaja Ranjit Singh Punjab Technical University – sequence: 4 givenname: Ajay surname: Mittal fullname: Mittal, Ajay organization: University Institute of Engineering and Technology, Panjab University – sequence: 5 givenname: Krishan surname: Kumar fullname: Kumar, Krishan organization: University Institute of Engineering and Technology, Panjab University |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/36713767$$D View this record in MEDLINE/PubMed |
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| Keywords | Self-supervised Un-supervised learning Supervised learning Contrastive learning Machine learning |
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| SubjectTerms | Algorithms Artificial intelligence Computer vision Datasets Engineering Image processing Image segmentation Labeling Literature reviews Machine learning Mathematical and Computational Engineering Natural language processing Object recognition Review Review Article Self-supervised learning Software Supervision Unstructured data Unsupervised learning |
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