Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Daudzpusīgā apguve× | Pašuzraudzības apmācība× | |
|---|---|---|
| Nozare | Mašīnmācīšanās | Mašīnmācīšanās |
| Saime | Machine learning | Machine learning |
| Izcelsmes gads≠ | 1970s–2006 (formalized) | 2018–2020 |
| Autors≠ | Vapnik, V. N. and others (community of researchers, 1970s–2000s) | LeCun, Y. and community (formalized ~2018–2020) |
| Tips≠ | Learning paradigm | Representation learning paradigm |
| Pirmavots≠ | Chapelle, O., Scholkopf, B., & Zien, A. (Eds.) (2006). Semi-Supervised Learning. MIT Press. ISBN: 978-0-262-03358-9 | 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 | SSL, semi-supervised machine learning, transductive learning, label-efficient learning | SSL, self-supervised pre-training, pretext-task learning, unsupervised representation learning |
| Saistītās≠ | 5 | 3 |
| Kopsavilkums≠ | Semi-supervised learning (SSL) is a machine learning paradigm that trains models using a small set of labeled examples together with a much larger pool of unlabeled data. By leveraging the structure inherent in unlabeled data, SSL achieves accuracy closer to fully supervised models while requiring far fewer costly manual labels — making it practical when labeling is expensive, slow, or resource-constrained. | 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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