  Metric learning is a widely used method for few shot learning in which the
quality of prototypes plays a key role in the algorithm. In this paper we
propose the trainable prototypes for distance measure instead of the artificial
ones within the meta-training and task-training framework. Also to avoid the
disadvantages that the episodic meta-training brought, we adopt non-episodic
meta-training based on self-supervised learning. Overall we solve the few-shot
tasks in two phases: meta-training a transferable feature extractor via
self-supervised learning and training the prototypes for metric classification.
In addition, the simple attention mechanism is used in both meta-training and
task-training. Our method achieves state-of-the-art performance in a variety of
established few-shot tasks on the standard few-shot visual classification
dataset, with about 20% increase compared to the available unsupervised
few-shot learning methods.
