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| Bayesianisches Transfer-Lernen× | Few-Shot Learning× | |
|---|---|---|
| Fachgebiet | Maschinelles Lernen | Maschinelles Lernen |
| Familie | Machine learning | Machine learning |
| Entstehungsjahr≠ | 2006–2010 | 2011–2017 |
| Urheber≠ | Raina, R.; Ng, A. Y.; Koller, D. (and subsequent community) | Lake, B. M.; Vinyals, O.; Finn, C. et al. |
| Typ≠ | Probabilistic transfer / domain adaptation framework | Meta-learning / low-data learning paradigm |
| Wegweisende Quelle≠ | Raina, R., Ng, A. Y., & Koller, D. (2006). Constructing informative priors using transfer learning. In Proceedings of the 23rd International Conference on Machine Learning (ICML), pp. 713–720. ACM. 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 | BTL, Bayesian domain adaptation, probabilistic transfer learning, Bayesian knowledge transfer | FSL, low-shot learning, k-shot learning, meta-learning for few examples |
| Verwandt | 4 | 4 |
| Zusammenfassung≠ | Bayesian Transfer Learning is a probabilistic framework that uses knowledge from a data-rich source domain to construct informative priors for a model trained on a data-scarce target domain. By encoding source-domain knowledge as prior distributions over parameters, the framework lets the model generalize well on the target task even with very limited labeled examples. | 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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