Model-agnostic meta-learning (MAML) is arguably the most popular meta-learning algorithm nowadays, given its flexibility to incorporate various model architectures and to be applied to different problems. Nevertheless, its performance on few-shot classification is far behind many recent algorithms dedicated to the problem. In this paper, we point out several key facets of how to train MAML to excel in few-shot classification. First, we find that a large number of gradient steps are needed for the inner loop update, which contradicts the common usage of MAML for few-shot classification. Second, we find that MAML is sensitive to the permutation of class assignments in meta-testing: for a few-shot task of $N$ classes, there are exponentially many ways to assign the learned initialization of the $N$-way classifier to the $N$ classes, leading to an unavoidably huge variance. Third, we investigate several ways for permutation invariance and find that learning a shared classifier initialization for all the classes performs the best. On benchmark datasets such as MiniImageNet and TieredImageNet, our approach, which we name UNICORN-MAML, performs on a par with or even outperforms state-of-the-art algorithms, while keeping the simplicity of MAML without adding any extra sub-networks.

在Few-shot classification这个问题上，我们探究了如何训练MAML表现得更美好，发现MAML需要大量的梯度步骤才能适应少样本分类，敏感于试验时分类标签的赋值，本文提供了多种方法解决其排列不变性。我们命名为 UNICORN-MAML 的方法在MiniImageNet和TieriImageNet等基准数据集上表现良好，并与许多最新的少样本分类算法不相上下，而不损失MAML的简单性。

如何训练你的 MAML 在少样本分类领域表现卓越