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, meta-learning
typically uses shallow neural networks (SNNs), thus limiting its effectiveness.
In this paper we propose a novel few-shot learning method called meta-transfer
learning (MTL) which learns to adapt 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 for MTL. We conduct experiments using (5-class,
1-shot) and (5-class, 5-shot) recognition tasks on two challenging few-shot
learning benchmarks: miniImageNet and Fewshot-CIFAR100. Extensive comparisons
to related works validate that our meta-transfer learning 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.

本篇论文提出了一种名为元转移学习的新型少样本学习方法，通过训练多个任务以及为每个任务学习深度神经网络权重的缩放和移位函数来实现迁移。作者使用提出的 HT 元批处理方案对 MiniImagenet 和 Fewshot-CIFAR100 这两个具有挑战性的少样本学习基准进行了实验，并将其与相关工作进行了广泛比较，结果验证了元转移学习方法的优越性和高准确性。