Diversity Helps: Unsupervised Few-shot Learning via Distribution Shift-based Data Augmentation

Understand

Few-shot learning aims to learn a new concept when only a few training examples are available, which has been extensively explored in recent years.

• However, most of the current works heavily rely on a large-scale labeled auxiliary set to train their models in an episodic-training paradigm.
• Such a kind of supervised setting basically limits the widespread use of few-shot learning algorithms.
• Instead, in this paper, we develop a novel framework called Unsupervised Few-shot Learning via Distribution Shift-based Data Augmentation (ULDA), which pays attention to the distribution diversity inside each constructed pretext few-shot task when using data augmentation.