Salīdzināt metodes
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| Ievads puspārraudzītā metrikas apguvē× | Pašuzraudzības apmācība× | |
|---|---|---|
| Nozare | Mašīnmācīšanās | Mašīnmācīšanās |
| Saime | Machine learning | Machine learning |
| Izcelsmes gads≠ | 2007–2008 | 2018–2020 |
| Autors≠ | Yeung, D.-Y. & Chang, H.; Davis, J. V. & Dhillon, I. S. | LeCun, Y. and community (formalized ~2018–2020) |
| Tips≠ | Hybrid supervised/unsupervised distance learning | Representation learning paradigm |
| Pirmavots≠ | Yeung, D.-Y., & Chang, H. (2007). A kernel approach for semi-supervised metric learning. IEEE Transactions on Neural Networks, 18(1), 141–149. DOI ↗ | LeCun, Y. & Misra, I. (2022). Self-supervised learning: The dark matter of intelligence. Meta AI Blog. https://ai.facebook.com/blog/self-supervised-learning-the-dark-matter-of-intelligence/ link ↗ |
| Citi nosaukumi | SSML, semi-supervised distance learning, constrained metric learning, weakly supervised metric learning | SSL, self-supervised pre-training, pretext-task learning, unsupervised representation learning |
| Saistītās≠ | 5 | 3 |
| Kopsavilkums≠ | Semi-supervised metric learning learns a task-adapted distance function by combining a small set of labeled pairwise constraints — must-link and cannot-link pairs — with the geometric structure of a much larger pool of unlabeled data. The result is a Mahalanobis-style or kernel-based distance that reflects both supervision and data topology, improving downstream tasks such as nearest-neighbor classification and clustering. | Self-supervised learning (SSL) is a machine-learning paradigm that generates its own supervisory signal directly from unlabeled data by defining an auxiliary pretext task — such as predicting masked words, rotating images, or contrasting augmented views — and uses the learned representations as a powerful starting point for downstream tasks with minimal labeled examples. |
| ScholarGateDatu kopa ↗ |
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