  Meta-learning has been proposed as a framework to address the challenging
few-shot learning setting. The key idea is to leverage a large number of
similar few-shot tasks in order to learn how to adapt a base-learner to a new
task for which only a few labeled samples are available. As deep neural
networks (DNNs) tend to overfit using a few samples only, typical meta-learning
models use shallow neural networks, thus limiting its effectiveness. In order
to achieve top performance, some recent works tried to use the DNNs pre-trained
on large-scale datasets but mostly in straight-forward manners, e.g., (1)
taking their weights as a warm start of meta-training, and (2) freezing their
convolutional layers as the feature extractor of base-learners. In this paper,
we propose a novel approach called meta-transfer learning (MTL) which learns to
transfer the weights of a deep NN for few-shot learning tasks. Specifically,
meta refers to training multiple tasks, and transfer is achieved by learning
scaling and shifting functions of DNN weights for each task. In addition, we
introduce the hard task (HT) meta-batch scheme as an effective learning
curriculum that further boosts the learning efficiency of MTL. We conduct
few-shot learning experiments and report top performance for five-class
few-shot recognition tasks on three challenging benchmarks: miniImageNet,
tieredImageNet and Fewshot-CIFAR100 (FC100). Extensive comparisons to related
works validate that our MTL approach trained with the proposed HT meta-batch
scheme achieves top performance. An ablation study also shows that both
components contribute to fast convergence and high accuracy.
