Meta-learning for few-shot learning entails acquiring a prior over previous
tasks and experiences, such that new tasks be learned from small amounts of
data. However, a critical challenge in few-shot learning is task ambiguity:
even when a powerful prior can be meta-learned from a large number of prior
tasks, a small dataset for a new task can simply be too ambiguous to acquire a
single model (e.g., a classifier) for that task that is accurate. In this
paper, we propose a probabilistic meta-learning algorithm that can sample
models for a new task from a model distribution. Our approach extends
model-agnostic meta-learning, which adapts to new tasks via gradient descent,
to incorporate a parameter distribution that is trained via a variational lower
bound. At meta-test time, our algorithm adapts via a simple procedure that
injects noise into gradient descent, and at meta-training time, the model is
trained such that this stochastic adaptation procedure produces samples from
the approximate model posterior. Our experimental results show that our method
can sample plausible classifiers and regressors in ambiguous few-shot learning
problems. We also show how reasoning about ambiguity can also be used for
downstream active learning problems.

该论文提出了一种概率元学习算法，能够从模型分布中采样模型，并且在模型适应新任务时注入噪声来减少任务模糊性，实验结果表明，该方法可以在模糊的少样本学习问题中采样出可信的分类器和回归器，并且阐述了如何利用对模糊性的推理来解决活跃学习问题。