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Learning from limited exemplars (few-shot learning) is a fundamental,unsolved problem that has been laboriously explored in the machine learningcommunity. However, current few-shot learners are mostly supervised and relyheavily on a large amount of labeled examples. Unsupervised learning is a morenatural procedure for cognitive mammals and has produced promising results inmany machine learning tasks. In the current study, we develop a method to learnan unsupervised few-shot learner via self-supervised training (UFLST), whichcan effectively generalize to novel but related classes. The proposed modelconsists of two alternate processes, progressive clustering and episodictraining. The former generates pseudo-labeled training examples forconstructing episodic tasks; and the later trains the few-shot learner usingthe generated episodic tasks which further optimizes the featurerepresentations of data. The two processes facilitate with each other, andeventually produce a high quality few-shot learner. Using the benchmark datasetOmniglot and Mini-ImageNet, we show that our model outperforms otherunsupervised few-shot learning methods. Using the benchmark dataset Market1501,we further demonstrate the feasibility of our model to a real-world applicationon person re-identification.

Unsupervised Few-shot Learning via Self-supervised Training