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

26 Feb 2019arXiv:1902.09884archive 2025-07-28

Antreas Antoniou, Amos Storkey

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.

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Tasks

Data Augmentation Few-Shot Learning Meta-Learning Unsupervised Few-Shot Image Classification Unsupervised Few-Shot Learning

Results from the paper archive 2025-07-28

Task	Dataset	Model	Metric	Value	Rank at snapshot	Leaderboard	Report
Unsupervised Few-Shot Image Classification	Mini-Imagenet 5-way (1-shot)	AAL	Accuracy	37.67	#28 of 28	Archive leaderboard	report
Unsupervised Few-Shot Image Classification	Mini-Imagenet 5-way (5-shot)	AAL	Accuracy	49.18	#28 of 28	Archive leaderboard	report

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