Ensemble Few-Shot Learning
Ensemble Few-Shot Learning kombinerer flere few-shot modeller — såsom prototyperiske netværk eller embedding-læringsmodeller — til at klassificere nye klasser ud fra kun et til en håndfuld mærkede eksempler. Ved at håndhæve diversitet blandt basemodellerne og aggregere deres forudsigelser, overgår ensemblet konsekvent enhver enkelt few-shot model i nøjagtighed og robusthed, især under alvorlig mangel på labels.
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Method map
The neighbourhood of related methods — select a node to explore.
Kilder
- 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 ↗
- Wang, Y., Yao, Q., Kwok, J. T., & Ni, L. M. (2020). Generalizing from a Few Examples: A Survey on Few-Shot Learning. ACM Computing Surveys, 53(3), 1–34. DOI: 10.1145/3386252 ↗
Sådan citerer du denne side
ScholarGate. (2026, June 3). Ensemble Methods for Few-Shot Learning. ScholarGate. https://scholargate.app/da/machine-learning/ensemble-few-shot-learning
Which method?
Set this method beside its closest kin and read them side by side — the library lays the books on the table; the choice is yours.
- BoostingMaskinlæring↔ compare
- Few-shot LearningMaskinlæring↔ compare
- Semi-supervised Few-shot LearningMaskinlæring↔ compare
- OverførselslæringMaskinlæring↔ compare
- StemmeensembleMaskinlæring↔ compare
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