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| Enesest juhendatud võimendamine× | Enesest juhendav õppimine× | |
|---|---|---|
| Valdkond | Masinõpe | Masinõpe |
| Perekond | Machine learning | Machine learning |
| Tekkeaasta≠ | 2010s–2020s | 2018–2020 |
| Looja≠ | Various researchers (2010s–2020s) | LeCun, Y. and community (formalized ~2018–2020) |
| Tüüp≠ | Ensemble (self-supervised + boosting) | Representation learning paradigm |
| Algallikas≠ | Yarowsky, D. (1995). Unsupervised word sense disambiguation rivaling supervised methods. In Proceedings of the 33rd Annual Meeting of the Association for Computational Linguistics (pp. 189–196). ACL. link ↗ | 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 ↗ |
| Rööpnimetused | SSL boosting, self-supervised ensemble boosting, pretext-task boosting, SSL-Boost | SSL, self-supervised pre-training, pretext-task learning, unsupervised representation learning |
| Seotud≠ | 6 | 3 |
| Kokkuvõte≠ | Self-supervised boosting integrates self-supervised pretext tasks into the boosting framework — covering AdaBoost, gradient boosting, and their modern variants — to leverage large pools of unlabeled data. By first learning feature representations from unlabeled samples and then running sequential weak-learner ensembles on pseudo-labeled data, it achieves competitive accuracy even when ground-truth labels are scarce. | 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. |
| ScholarGateAndmestik ↗ |
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