Diversity Helps: Unsupervised Few-shot Learning via Distribution Shift-based Data Augmentation
Published in
(2020)External Links:
TL
TL;DR
This paper develops 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.