Few-shot supervised learning leverages experience from previous learning tasks to solve new tasks where only a few labelled examples are available. One successful line of approach to this problem is to use an encoder-decoder meta-learning pipeline, whereby labelled data in a task is encoded to produce task representation, and this representation is used to condition the decoder to make predictions on unlabelled data. We propose an approach that uses this pipeline with two important features. 1) We use infinite-dimensional functional representations of the task rather than fixed-dimensional representations. 2) We iteratively apply functional updates to the representation. We show that our approach can be interpreted as extending functional gradient descent, and delivers performance that is comparable to or outperforms previous state-of-the-art on few-shot classification benchmarks such as miniImageNet and tieredImageNet.

开发了一种功能编码器-解码器方法来进行监督式元学习，在其中标记的数据被编码为无限维的函数式表示而不是有限维的表示。此外，我们学习了一种神经更新规则，类似于函数梯度下降，它迭代地改进该表示。我们的方法首次证明了编码器-解码器式元学习方法（如有条件的神经过程）在大规模few-shot分类基准测试上的成功应用，例如miniImageNet和tieredImageNet，在其中实现了最先进的性能。

MetaFun：使用迭代函数更新的元学习