  The field of few-shot learning has been laboriously explored in the
supervised setting, where per-class labels are available. On the other hand,
the unsupervised few-shot learning setting, where no labels of any kind are
required, has seen little investigation. We propose a method, named Assume,
Augment and Learn or AAL, for generating few-shot tasks using unlabeled data.
We randomly label a random subset of images from an unlabeled dataset to
generate a support set. Then by applying data augmentation on the support set's
images, and reusing the support set's labels, we obtain a target set. The
resulting few-shot tasks can be used to train any standard meta-learning
framework. Once trained, such a model, can be directly applied on small
real-labeled datasets without any changes or fine-tuning required. In our
experiments, the learned models achieve good generalization performance in a
variety of established few-shot learning tasks on Omniglot and Mini-Imagenet.
