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| Ensemble Few-Shot Learning× | Mācīšanās ar maziem paraugu skaitu× | |
|---|---|---|
| Nozare | Mašīnmācīšanās | Mašīnmācīšanās |
| Saime | Machine learning | Machine learning |
| Izcelsmes gads≠ | 2019 | 2011–2017 |
| Autors≠ | Dvornik, N., Schmid, C., & Mairal, J. | Lake, B. M.; Vinyals, O.; Finn, C. et al. |
| Tips≠ | Ensemble of few-shot learners | Meta-learning / low-data learning paradigm |
| Pirmavots≠ | Dvornik, N., Schmid, C., & Mairal, J. (2019). Diversity with Cooperation: Ensemble Methods for Few-Shot Classification. In Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), pp. 3716–3725. 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 ↗ |
| Citi nosaukumi | ensemble few-shot classification, multi-model few-shot learning, few-shot ensemble, cooperative few-shot ensemble | FSL, low-shot learning, k-shot learning, meta-learning for few examples |
| Saistītās≠ | 5 | 4 |
| Kopsavilkums≠ | Ensemble Few-Shot Learning combines multiple few-shot models — such as prototypical networks or embedding learners — to classify new classes from only one to a handful of labeled examples. By enforcing diversity among base learners and aggregating their predictions, the ensemble consistently outperforms any single few-shot model in accuracy and robustness, especially under severe label scarcity. | 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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