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Ensemble Few-shot learning

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.

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Ensemble Methods for Few-Shot Learning

Catatan metode taksonomi · ml-model / machine-learning

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