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| Online Few-shot Learning× | Few-shot Learning× | |
|---|---|---|
| Fagområde | Maskinlæring | Maskinlæring |
| Familie | Machine learning | Machine learning |
| Oprindelsesår≠ | 2019 | 2011–2017 |
| Ophavsperson≠ | Finn, C. et al. (online meta-learning formalization) | Lake, B. M.; Vinyals, O.; Finn, C. et al. |
| Type≠ | Online learning + meta-learning hybrid | Meta-learning / low-data learning paradigm |
| Oprindelig kilde≠ | Finn, C., Rajeswaran, A., Kakade, S., & Levine, S. (2019). Online Meta-Learning. Proceedings of the 36th International Conference on Machine Learning (ICML), PMLR 97, 1920–1930. 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 ↗ |
| Aliasser | online meta-learning, streaming few-shot learning, continual few-shot learning, incremental few-shot learning | FSL, low-shot learning, k-shot learning, meta-learning for few examples |
| Relaterede | 4 | 4 |
| Resumé≠ | Online Few-shot Learning combines the streaming update principle of online learning with the data-efficiency goal of few-shot learning, enabling a model to continuously adapt to new tasks or classes from only a handful of labeled examples as data arrives sequentially — without access to the full historical dataset. | 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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