A Self-Supervised Deep Learning Framework for Unsupervised Few-Shot Learning and Clustering

Abstract

The need to learn a good representation is a core problem central to AI. We present a self-supervised representation learning framework and demonstrate its use for few-shot classification and clustering. Our framework can be interpreted as repeatedly discovering new categories from learned embeddings and training a new embedding function with self-supervised signals to differentiate the discovered categories. In our framework, we first discover categories from unlabeled data. Next we post-process the previous partition results to remove outliers and derive prototypes of each category. We then construct few-shot learning tasks with previously selected data and augmented virtual data. Lastly, we iterative train the network through previous steps to learn the final representation. Our framework can considerably outperform previous baselines in unsupervised few-shot classification tasks on miniImageNet and Omniglot data sets. We also validate our learned representation on clustering tasks and demonstrate that our framework further improves upon the recent deep clustering methods.