Meta-Learning with Differentiable Convex Optimization
Abstract
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. Our code is available at https://github.com/kjunelee/MetaOptNet.
Cite
@article{arxiv.1904.03758,
title = {Meta-Learning with Differentiable Convex Optimization},
author = {Kwonjoon Lee and Subhransu Maji and Avinash Ravichandran and Stefano Soatto},
journal= {arXiv preprint arXiv:1904.03758},
year = {2019}
}
Comments
Accepted to CVPR 2019 (Oral)