Сравнение на методи
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| Полу-обучено трансферно обучение× | Трансферно обучение× | |
|---|---|---|
| Област | Машинно обучение | Машинно обучение |
| Семейство | Machine learning | Machine learning |
| Година на възникване≠ | 2010s | 2010 (formalized); 1990s (early roots) |
| Създател≠ | Pan, S. J. & Yang, Q. (formalized); wider community | Pan, S. J. & Yang, Q. (survey); Bengio, Y. (deep learning framing) |
| Тип≠ | Hybrid learning paradigm | Learning paradigm |
| Основополагащ източник≠ | 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 ↗ |
| Други названия | SSTL, semi-supervised domain adaptation, transfer learning with unlabeled data, few-label transfer learning | TL, domain adaptation, fine-tuning, pre-trained model adaptation |
| Свързани≠ | 4 | 3 |
| Резюме≠ | 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. |
| ScholarGateНабор от данни ↗ |
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