Group-based siamese self-supervised learning
In this paper, we introduced a novel group self-supervised learning approach designed to improve visual representation learning. This new method aimed to rectify the limitations observed in conventional self-supervised learning. Traditional methods tended to focus on embedding distortion-invariant i...
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| Format: | Article |
| Language: | English |
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AIMS Press
2024-08-01
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| Series: | Electronic Research Archive |
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| Online Access: | https://www.aimspress.com/article/doi/10.3934/era.2024226 |
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| author | Zhongnian Li Jiayu Wang Qingcong Geng Xinzheng Xu |
| author_facet | Zhongnian Li Jiayu Wang Qingcong Geng Xinzheng Xu |
| author_sort | Zhongnian Li |
| collection | DOAJ |
| description | In this paper, we introduced a novel group self-supervised learning approach designed to improve visual representation learning. This new method aimed to rectify the limitations observed in conventional self-supervised learning. Traditional methods tended to focus on embedding distortion-invariant in single-view features. However, our belief was that a better representation can be achieved by creating a group of features derived from multiple views. To expand the siamese self-supervised architecture, we increased the number of image instances in each crop, enabling us to obtain an average feature from a group of views to use as a distortion, invariant embedding. The training efficiency has greatly increased with rapid convergence. When combined with a robust linear protocol, this group self-supervised learning model achieved competitive results in CIFAR-10, CIFAR-100, Tiny ImageNet, and ImageNet-100 classification tasks. Most importantly, our model demonstrated significant convergence gains within just 30 epochs as opposed to the typical 1000 epochs required by most other self-supervised techniques. |
| format | Article |
| id | doaj-art-544b1122be244001a1ddaa61186fa09e |
| institution | Kabale University |
| issn | 2688-1594 |
| language | English |
| publishDate | 2024-08-01 |
| publisher | AIMS Press |
| record_format | Article |
| series | Electronic Research Archive |
| spelling | doaj-art-544b1122be244001a1ddaa61186fa09e2025-01-23T07:51:27ZengAIMS PressElectronic Research Archive2688-15942024-08-013284913492510.3934/era.2024226Group-based siamese self-supervised learningZhongnian Li0Jiayu Wang1Qingcong Geng2Xinzheng Xu3School of Computer Science and Technology, China University of Mining and Technology, Xuzhou 221116, ChinaSchool of Computer Science and Technology, China University of Mining and Technology, Xuzhou 221116, ChinaSchool of Computer Science and Technology, China University of Mining and Technology, Xuzhou 221116, ChinaSchool of Computer Science and Technology, China University of Mining and Technology, Xuzhou 221116, ChinaIn this paper, we introduced a novel group self-supervised learning approach designed to improve visual representation learning. This new method aimed to rectify the limitations observed in conventional self-supervised learning. Traditional methods tended to focus on embedding distortion-invariant in single-view features. However, our belief was that a better representation can be achieved by creating a group of features derived from multiple views. To expand the siamese self-supervised architecture, we increased the number of image instances in each crop, enabling us to obtain an average feature from a group of views to use as a distortion, invariant embedding. The training efficiency has greatly increased with rapid convergence. When combined with a robust linear protocol, this group self-supervised learning model achieved competitive results in CIFAR-10, CIFAR-100, Tiny ImageNet, and ImageNet-100 classification tasks. Most importantly, our model demonstrated significant convergence gains within just 30 epochs as opposed to the typical 1000 epochs required by most other self-supervised techniques.https://www.aimspress.com/article/doi/10.3934/era.2024226self-supervised learningaverage featuremultiple viewsclassification taskssiamese network |
| spellingShingle | Zhongnian Li Jiayu Wang Qingcong Geng Xinzheng Xu Group-based siamese self-supervised learning Electronic Research Archive self-supervised learning average feature multiple views classification tasks siamese network |
| title | Group-based siamese self-supervised learning |
| title_full | Group-based siamese self-supervised learning |
| title_fullStr | Group-based siamese self-supervised learning |
| title_full_unstemmed | Group-based siamese self-supervised learning |
| title_short | Group-based siamese self-supervised learning |
| title_sort | group based siamese self supervised learning |
| topic | self-supervised learning average feature multiple views classification tasks siamese network |
| url | https://www.aimspress.com/article/doi/10.3934/era.2024226 |
| work_keys_str_mv | AT zhongnianli groupbasedsiameseselfsupervisedlearning AT jiayuwang groupbasedsiameseselfsupervisedlearning AT qingconggeng groupbasedsiameseselfsupervisedlearning AT xinzhengxu groupbasedsiameseselfsupervisedlearning |