  Most previous few-shot learning algorithms are based on meta-training with
fake few-shot tasks as training samples, where large labeled base classes are
required. The trained model is also limited by the type of tasks. In this paper
we propose a new paradigm of unsupervised few-shot learning to repair the
deficiencies. We solve the few-shot tasks in two phases: meta-training a
transferable feature extractor via contrastive self-supervised learning and
training a classifier using graph aggregation, self-distillation and manifold
augmentation. Once meta-trained, the model can be used in any type of tasks
with a task-dependent classifier training. Our method achieves state of-the-art
performance in a variety of established few-shot tasks on the standard few-shot
visual classification datasets, with an 8- 28% increase compared to the
available unsupervised few-shot learning methods.
