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| 자기 지도 학습× | 전이 학습× | |
|---|---|---|
| 분야 | 머신러닝 | 머신러닝 |
| 계열 | Machine learning | Machine learning |
| 기원 연도≠ | 2018–2020 | 2010 (formalized); 1990s (early roots) |
| 창시자≠ | LeCun, Y. and community (formalized ~2018–2020) | Pan, S. J. & Yang, Q. (survey); Bengio, Y. (deep learning framing) |
| 유형≠ | Representation learning paradigm | Learning paradigm |
| 원전≠ | 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 ↗ | Pan, S. J., & Yang, Q. (2010). A Survey on Transfer Learning. IEEE Transactions on Knowledge and Data Engineering, 22(10), 1345–1359. DOI ↗ |
| 별칭 | SSL, self-supervised pre-training, pretext-task learning, unsupervised representation learning | TL, domain adaptation, fine-tuning, pre-trained model adaptation |
| 관련 | 3 | 3 |
| 요약≠ | 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. | Transfer learning is a machine learning paradigm in which knowledge gained from training a model on a source task or domain is reused to improve learning on a different but related target task or domain. It is especially powerful when labeled data for the target task is scarce, and it underlies most modern deep learning applications in computer vision, natural language processing, and beyond. |
| ScholarGate데이터셋 ↗ |
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