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| Ensemble Few-Shot Learning× | Boosting× | |
|---|---|---|
| Fachgebiet | Maschinelles Lernen | Maschinelles Lernen |
| Familie | Machine learning | Machine learning |
| Entstehungsjahr≠ | 2019 | 1990–1997 |
| Urheber≠ | Dvornik, N., Schmid, C., & Mairal, J. | Schapire, R. E.; Freund, Y. |
| Typ≠ | Ensemble of few-shot learners | Sequential ensemble (iterative reweighting) |
| Wegweisende Quelle≠ | 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 ↗ | Freund, Y. & Schapire, R. E. (1997). A decision-theoretic generalization of on-line learning and an application to boosting. Journal of Computer and System Sciences, 55(1), 119–139. DOI ↗ |
| Aliasnamen | ensemble few-shot classification, multi-model few-shot learning, few-shot ensemble, cooperative few-shot ensemble | AdaBoost, gradient boosting, iterative reweighting ensemble, sequential ensemble |
| Verwandt≠ | 5 | 6 |
| Zusammenfassung≠ | 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. | Boosting is a sequential ensemble technique that converts many simple, barely-better-than-chance learners into a single highly accurate model by repeatedly focusing training on the examples that previous learners got wrong, then combining all learners with weights proportional to their individual accuracy. |
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