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Self-supervised learning (SSL) is a method of machine learning. It learns from unlabeled sample data. It can be regarded as an intermediate form between supervised and unsupervised learning. It is based on an artificial neural network or other model such as a decision list. The model learns in two steps. First, the task is solved based on pseudo-labels which help to initialize the model parameters. Second, the actual task is performed with supervised or unsupervised learning. Self-supervised learning has produced promising results in recent years and has found practical application in audio processing and is being used by Facebook and others for speech recognition. The primary appeal of SSL is that training can occur with data of lower quality, rather than improving ultimate outcomes. Self-supervised learning more closely imitates the way humans learn to classify objects.
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References
- ↑ Gao, Zhanning; Wang, Le; Jojic, Nebojsa; Niu, Zhenxing; Zheng, Nanning; Hua, Gang (2019-12-01). "Video Imprint". IEEE Transactions on Pattern Analysis and Machine Intelligence. Institute of Electrical and Electronics Engineers (IEEE). 41 (12): 3086–3099. arXiv: 2106.03283 . doi:10.1109/tpami.2018.2866114. ISSN 0162-8828. PMID 30130178. S2CID 52059105.
- 1 2 Gao, Zhanning; Wang, Le; Zhang, Qilin; Niu, Zhenxing; Zheng, Nanning; Hua, Gang (2019-07-17). "Video Imprint Segmentation for Temporal Action Detection in Untrimmed Videos" (PDF). Proceedings of the AAAI Conference on Artificial Intelligence. 33 (1): 8328–8335. doi: 10.1609/aaai.v33i01.33018328 . ISSN 2374-3468.
- ↑ Jojic, N.; Frey, B.J.; Kannan, A. (2003). "Epitomic analysis of appearance and shape". Proceedings Ninth IEEE International Conference on Computer Vision. IEEE ICCV. pp. 34-41 vol.1. doi:10.1109/iccv.2003.1238311. ISBN 0-7695-1950-4.
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