Learning the Relation Between Similarity Loss and Clustering Loss in Self-Supervised Learning
Self-supervised learning enables networks to learn discriminative features from massive data itself. Most state-of-the-art methods maximize the similarity between two augmentations of one image based on contrastive learning. By utilizing the consistency of two augmentations, the burden of manual ann...
Ausführliche Beschreibung
Bibliographische Detailangaben
| Veröffentlicht in: | IEEE transactions on image processing : a publication of the IEEE Signal Processing Society. - 1992. - 32(2023) vom: 25., Seite 3442-3454
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| 1. Verfasser: |
Ge, Jidong
(VerfasserIn) |
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| Weitere Verfasser: |
Liu, Yuxiang,
Gui, Jie,
Fang, Lanting,
Lin, Ming,
Kwok, James Tin-Yau,
Huang, Liguo,
Luo, Bin |
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| Format: | Online-Aufsatz
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| Sprache: | English |
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| Veröffentlicht: |
2023
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| Zugriff auf das übergeordnete Werk: | IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
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| Schlagworte: | Journal Article |
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