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Abstract

Meta-learning has been shown to be an effective strategy for few-shot
learning. The key idea is to leverage a large number of similar few-shot tasks
in order to meta-learn how to best initiate a (single) base-learner for novel
few-shot tasks. While meta-learning how to initialize a base-learner has shown
promising results, it is well known that hyperparameter settings such as the
learning rate and the weighting of the regularization term are important to
achieve best performance. We thus propose to also meta-learn these
hyperparameters and in fact learn a time- and layer-varying scheme for learning
a base-learner on novel tasks. Additionally, we propose to learn not only a
single base-learner but an ensemble of several base-learners to obtain more
robust results. While ensembles of learners have shown to improve performance
in various settings, this is challenging for few-shot learning tasks due to the
limited number of training samples. Therefore, our approach also aims to
meta-learn how to effectively combine several base-learners. We conduct
extensive experiments and report top performance for five-class few-shot
recognition tasks on two challenging benchmarks: miniImageNet and
Fewshot-CIFAR100 (FC100).