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| Ensemble Few-Shot Learning× | Pārneses apmācība× | |
|---|---|---|
| Nozare | Mašīnmācīšanās | Mašīnmācīšanās |
| Saime | Machine learning | Machine learning |
| Izcelsmes gads≠ | 2019 | 2010 (formalized); 1990s (early roots) |
| Autors≠ | Dvornik, N., Schmid, C., & Mairal, J. | Pan, S. J. & Yang, Q. (survey); Bengio, Y. (deep learning framing) |
| Tips≠ | Ensemble of few-shot learners | 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 ↗ | Pan, S. J., & Yang, Q. (2010). A Survey on Transfer Learning. IEEE Transactions on Knowledge and Data Engineering, 22(10), 1345–1359. DOI ↗ |
| Citi nosaukumi | ensemble few-shot classification, multi-model few-shot learning, few-shot ensemble, cooperative few-shot ensemble | TL, domain adaptation, fine-tuning, pre-trained model adaptation |
| Saistītās≠ | 5 | 3 |
| 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. | 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. |
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