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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Zusammenfassung: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 representations for few-shot learning and show they offer better tradeoffs between feature size and performance across a range of few-shot recognition benchmarks. Our objective is to learn feature embeddings that generalize well under a linear classification rule for novel categories. To efficiently solve the objective, we exploit two properties of linear classifiers: implicit differentiation of the optimality conditions of the convex problem and the dual formulation of the optimization problem. This allows us to use high-dimensional embeddings with improved generalization at a modest increase in computational overhead. Our approach, named MetaOptNet, achieves state-of-the-art performance on miniImageNet, tieredImageNet, CIFAR-FS, and FC100 few-shot learning benchmarks.
ISSN:1063-6919
DOI:10.1109/CVPR.2019.01091