  Few-shot or one-shot learning of classifiers requires a significant inductive
bias towards the type of task to be learned. One way to acquire this is by
meta-learning on tasks similar to the target task. In this paper, we propose
UMTRA, an algorithm that performs unsupervised, model-agnostic meta-learning
for classification tasks. The meta-learning step of UMTRA is performed on a
flat collection of unlabeled images. While we assume that these images can be
grouped into a diverse set of classes and are relevant to the target task, no
explicit information about the classes or any labels are needed. UMTRA uses
random sampling and augmentation to create synthetic training tasks for
meta-learning phase. Labels are only needed at the final target task learning
step, and they can be as little as one sample per class. On the Omniglot and
Mini-Imagenet few-shot learning benchmarks, UMTRA outperforms every tested
approach based on unsupervised learning of representations, while alternating
for the best performance with the recent CACTUs algorithm. Compared to
supervised model-agnostic meta-learning approaches, UMTRA trades off some
classification accuracy for a reduction in the required labels of several
orders of magnitude.
