Compară metode
Examinează metodele selectate una lângă alta; rândurile care diferă sunt evidențiate.
| Învățare prin transfer semi-supervizată× | Învățare prin transfer× | |
|---|---|---|
| Domeniu | Învățare automată | Învățare automată |
| Familie | Machine learning | Machine learning |
| Anul apariției≠ | 2010s | 2010 (formalized); 1990s (early roots) |
| Autorul original≠ | Pan, S. J. & Yang, Q. (formalized); wider community | Pan, S. J. & Yang, Q. (survey); Bengio, Y. (deep learning framing) |
| Tip≠ | Hybrid learning paradigm | Learning paradigm |
| Sursa seminală≠ | Zhuang, F., Qi, Z., Duan, K., Xi, D., Zhu, Y., Zhu, H., Xiong, H., & He, Q. (2021). A comprehensive survey on transfer learning. Proceedings of the IEEE, 109(1), 43–76. DOI ↗ | Pan, S. J., & Yang, Q. (2010). A Survey on Transfer Learning. IEEE Transactions on Knowledge and Data Engineering, 22(10), 1345–1359. DOI ↗ |
| Denumiri alternative | SSTL, semi-supervised domain adaptation, transfer learning with unlabeled data, few-label transfer learning | TL, domain adaptation, fine-tuning, pre-trained model adaptation |
| Înrudite≠ | 4 | 3 |
| Rezumat≠ | Semi-supervised Transfer Learning combines knowledge transferred from a richly labeled source domain with the structure of abundant unlabeled target-domain data, using only a small set of labeled target examples to achieve strong generalization where full annotation is scarce or expensive. | 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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