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| 정규화된 준지도 학습× | 자기 지도 학습× | |
|---|---|---|
| 분야 | 머신러닝 | 머신러닝 |
| 계열 | Machine learning | Machine learning |
| 기원 연도≠ | 2006 | 2018–2020 |
| 창시자≠ | Belkin, M.; Niyogi, P.; Sindhwani, V. | LeCun, Y. and community (formalized ~2018–2020) |
| 유형≠ | Regularized learning paradigm | Representation learning paradigm |
| 원전≠ | Belkin, M., Niyogi, P., & Sindhwani, V. (2006). Manifold regularization: A geometric framework for learning from labeled and unlabeled examples. Journal of Machine Learning Research, 7, 2399–2434. 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 | manifold regularization, graph-regularized SSL, semi-supervised regularization, Laplacian regularization | SSL, self-supervised pre-training, pretext-task learning, unsupervised representation learning |
| 관련≠ | 6 | 3 |
| 요약≠ | Regularized semi-supervised learning adds explicit geometric or graph-based penalty terms to a semi-supervised objective so that the decision function varies smoothly over the data manifold. Pioneered through manifold regularization (Belkin, Niyogi & Sindhwani, 2006), it exploits the structure of both labeled and unlabeled examples to learn more accurate models than supervised regularization alone when labeled data 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. |
| ScholarGate데이터셋 ↗ |
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