  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.

Computer Vision and Pattern Recognition;Machine Learning