Meta-Learning With Differentiable Convex Optimization

Many meta-learning approaches for few-shot learning rely on simple base learners such as nearest-neighbor classifiers. However, even in the few-shot regime, discriminatively trained linear predictors can offer better generalization. We propose to use these predictors as base learners to learn repres...

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Veröffentlicht in:	Proceedings (IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Online) S. 10649 - 10657
Hauptverfasser:	Lee, Kwonjoon, Maji, Subhransu, Ravichandran, Avinash, Soatto, Stefano
Format:	Tagungsbericht
Sprache:	Englisch
Veröffentlicht:	IEEE 01.06.2019
Schlagworte:	Benchmark testing Computational efficiency Computational modeling Deep Learning Few shot learning Memory management Metalearning Optimization Overfitting Pattern recognition Representation Learning Training data
ISSN:	1063-6919
Online-Zugang:	Volltext
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