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| 다중 작업 학습 (Multitask Learning, MTL)× | 전이 학습× | |
|---|---|---|
| 분야≠ | 딥러닝 | 머신러닝 |
| 계열 | Machine learning | Machine learning |
| 기원 연도≠ | 1997 | 2010 (formalized); 1990s (early roots) |
| 창시자≠ | Rich Caruana | Pan, S. J. & Yang, Q. (survey); Bengio, Y. (deep learning framing) |
| 유형≠ | Inductive transfer method | Learning paradigm |
| 원전≠ | Caruana, R. (1997). Multitask learning. Machine Learning, 28(1), 41–75. DOI ↗ | Pan, S. J., & Yang, Q. (2010). A Survey on Transfer Learning. IEEE Transactions on Knowledge and Data Engineering, 22(10), 1345–1359. DOI ↗ |
| 별칭 | MTL, Joint Learning, Shared Representation Learning, Çok Görevli Öğrenme | TL, domain adaptation, fine-tuning, pre-trained model adaptation |
| 관련 | 3 | 3 |
| 요약≠ | Multitask Learning (MTL) is a machine learning paradigm in which a model is trained simultaneously on multiple related tasks, sharing representations across them to improve generalization. Introduced formally by Rich Caruana in 1997, MTL draws on the intuition that auxiliary tasks act as inductive bias, providing extra supervision signals that help the shared layers learn richer, more robust feature representations than single-task training would yield. | 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. |
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