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The field of few-shot learning has been laboriously explored in thesupervised setting, where per-class labels are available. On the other hand,the unsupervised few-shot learning setting, where no labels of any kind arerequired, 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 togenerate a support set. Then by applying data augmentation on the support set'simages, and reusing the support set's labels, we obtain a target set. Theresulting few-shot tasks can be used to train any standard meta-learningframework. Once trained, such a model, can be directly applied on smallreal-labeled datasets without any changes or fine-tuning required. In ourexperiments, the learned models achieve good generalization performance in avariety of established few-shot learning tasks on Omniglot and Mini-Imagenet.

Assume, Augment and Learn: Unsupervised Few-Shot Meta-Learning via Random Labels and Data Augmentation