Unsupervised Few-shot Learning via Self-supervised Training

Understand

Learning from limited exemplars (few-shot learning) is a fundamental, unsolved problem that has been laboriously explored in the machine learning community.

• However, current few-shot learners are mostly supervised and rely heavily on a large amount of labeled examples.
• Unsupervised learning is a more natural procedure for cognitive mammals and has produced promising results in many machine learning tasks.
• In the current study, we develop a method to learn an unsupervised few-shot learner via self-supervised training (UFLST), which can effectively generalize to novel but related classes.