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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Bibliographic Details

Published in:	Proceedings (IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Online) pp. 10649 - 10657
Main Authors:	Lee, Kwonjoon, Maji, Subhransu, Ravichandran, Avinash, Soatto, Stefano
Format:	Conference Proceeding
Language:	English
Published:	IEEE 01.06.2019
Subjects:	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 Access:	Get full text
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