Model-agnostic meta-learning (MAML) is arguably one of the most popular
meta-learning algorithms nowadays. 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 MAML needs a large number of
gradient steps in its inner loop update, which contradicts its common usage in
few-shot classification. Second, we find that MAML is sensitive to the class
label assignments during meta-testing. Concretely, MAML meta-trains the
initialization of an $N$-way classifier. These $N$ ways, during meta-testing,
then have "$N!$" different permutations to be paired with a few-shot task of
$N$ novel classes. We find that these permutations lead to a huge variance of
accuracy, making MAML unstable in few-shot classification. Third, we
investigate several approaches to make MAML permutation-invariant, among which
meta-training a single vector to initialize all the $N$ weight vectors in the
classification head performs the best. On benchmark datasets like MiniImageNet
and TieredImageNet, our approach, which we name UNICORN-MAML, performs on a par
with or even outperforms many recent few-shot classification algorithms,
without sacrificing MAML's simplicity.

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。