Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Daudzuzdevumu apguve× | Pārneses apmācība× | |
|---|---|---|
| Nozare≠ | Dziļā mācīšanās | Mašīnmācīšanās |
| Saime | Machine learning | Machine learning |
| Izcelsmes gads≠ | 1997 | 2010 (formalized); 1990s (early roots) |
| Autors≠ | Rich Caruana | Pan, S. J. & Yang, Q. (survey); Bengio, Y. (deep learning framing) |
| Tips≠ | Inductive transfer method | Learning paradigm |
| Pirmavots≠ | 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 ↗ |
| Citi nosaukumi | MTL, Joint Learning, Shared Representation Learning, Çok Görevli Öğrenme | TL, domain adaptation, fine-tuning, pre-trained model adaptation |
| Saistītās | 3 | 3 |
| Kopsavilkums≠ | 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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