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| Online Transfer learning× | Few-Shot Learning× | |
|---|---|---|
| Fachgebiet | Maschinelles Lernen | Maschinelles Lernen |
| Familie | Machine learning | Machine learning |
| Entstehungsjahr≠ | 2010 | 2011–2017 |
| Urheber≠ | Zhao, P. & Hoi, S. C. H. | Lake, B. M.; Vinyals, O.; Finn, C. et al. |
| Typ≠ | Online learning with source-domain knowledge transfer | Meta-learning / low-data learning paradigm |
| Wegweisende Quelle≠ | Zhao, P., & Hoi, S. C. H. (2010). OTL: A Framework of Online Transfer Learning. In Proceedings of the 27th International Conference on Machine Learning (ICML 2010), pp. 1231–1238. Omnipress. link ↗ | Vinyals, O., Blundell, C., Lillicrap, T., Wierstra, D., & Kavukcuoglu, K. (2016). Matching Networks for One Shot Learning. Advances in Neural Information Processing Systems (NeurIPS), 29. link ↗ |
| Aliasnamen | OTL, streaming transfer learning, incremental transfer learning, online domain adaptation | FSL, low-shot learning, k-shot learning, meta-learning for few examples |
| Verwandt | 4 | 4 |
| Zusammenfassung≠ | Online Transfer Learning (OTL) extends transfer learning to sequential, streaming settings: instead of training on a fixed dataset, the model processes examples one at a time and simultaneously leverages knowledge from a related source domain to improve predictions on the target domain without requiring large labeled target datasets upfront. | Few-shot learning is a machine learning paradigm that trains models to recognize new classes or solve new tasks from only a handful of labeled examples — typically one to five — by leveraging prior knowledge acquired from a large, related training distribution. It is especially relevant in domains where labeling is expensive, scarce, or structurally limited. |
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