Regulariseret Few-Shot Læring
Regulariseret few-shot læring udvider standard few-shot læringspipelines med eksplicitte regulariseringsmekanismer — såsom vægttab, dropout, dataaugmentation, label smoothing eller manifold-begrænsninger — for at reducere overfitting til de små support-sæt, der definerer hver episode. Dette producerer mere generaliserbare modeller, når kun én til tredive mærkede eksempler pr. klasse er tilgængelige.
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Method map
The neighbourhood of related methods — select a node to explore.
Kilder
- Chen, W., Liu, Y., Kira, Z., Wang, Y. F., & Huang, J. (2019). A Closer Look at Few-Shot Classification. International Conference on Learning Representations (ICLR). link ↗
- Tian, Y., Wang, Y., Krishnan, D., Tenenbaum, J. B., & Isola, P. (2020). Rethinking Few-Shot Image Classification: a Good Embedding Is All You Need? European Conference on Computer Vision (ECCV). link ↗
Sådan citerer du denne side
ScholarGate. (2026, June 3). Regularized Few-Shot Learning (Regularization-Enhanced Meta-Learning). ScholarGate. https://scholargate.app/da/machine-learning/regularized-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.
- Few-shot LearningMaskinlæring↔ compare
- Regulariseret TransferlæringMaskinlæring↔ compare
- Selvovervåget læringMaskinlæring↔ compare
- Semi-supervised Few-shot LearningMaskinlæring↔ compare
- OverførselslæringMaskinlæring↔ compare
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