Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Pašuzraudzības kārtībā apmācīts Random Forest× | Pašuzraudzības apmācība× | |
|---|---|---|
| Nozare | Mašīnmācīšanās | Mašīnmācīšanās |
| Saime | Machine learning | Machine learning |
| Izcelsmes gads≠ | 2012–2022 | 2018–2020 |
| Autors≠ | Lefortier, D. et al.; Criminisi, A. et al. (semi-supervised RF lineage) | LeCun, Y. and community (formalized ~2018–2020) |
| Tips≠ | Semi-supervised ensemble (self-supervised pretext task + RF) | Representation learning paradigm |
| Pirmavots≠ | Lefortier, D., Chitta, K., & Agrawal, P. (2022). Self-supervised random forests. arXiv:2204.01430. 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 ↗ |
| Citi nosaukumi | SSL-RF, self-supervised RF, self-supervised ensemble forest, unsupervised random forest with self-labeling | SSL, self-supervised pre-training, pretext-task learning, unsupervised representation learning |
| Saistītās≠ | 6 | 3 |
| Kopsavilkums≠ | Self-supervised Random Forest (SSL-RF) extends the classic random forest to settings where labeled examples are scarce. The forest is first trained using automatically generated pseudo-labels derived from a self-supervised pretext task — such as predicting data transformations or masked features — and then refined on whatever true labels are available, marrying the label-efficiency of self-supervised learning with the robustness of ensemble trees. | 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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