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| 자기 지도 랜덤 포레스트× | 자기 지도 학습× | |
|---|---|---|
| 분야 | 머신러닝 | 머신러닝 |
| 계열 | Machine learning | Machine learning |
| 기원 연도≠ | 2012–2022 | 2018–2020 |
| 창시자≠ | Lefortier, D. et al.; Criminisi, A. et al. (semi-supervised RF lineage) | LeCun, Y. and community (formalized ~2018–2020) |
| 유형≠ | Semi-supervised ensemble (self-supervised pretext task + RF) | Representation learning paradigm |
| 원전≠ | 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 ↗ |
| 별칭 | 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 |
| 관련≠ | 6 | 3 |
| 요약≠ | 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. |
| ScholarGate데이터셋 ↗ |
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