Meta-Learning for One-Class Classification with Few Examples using Order-Equivariant Network

Understand

This paper presents a meta-learning framework for few-shots One-Class Classification (OCC) at test-time, a setting where labeled examples are only available for the positive class, and no supervision is given for the negative example.

• We consider that we have a set of `one-class classification' objective-tasks with only a small set of positive examples available for each task, and a set of training tasks with full supervision (i.e.
• highly imbalanced classification).
• We propose an approach using order-equivariant networks to learn a 'meta' binary-classifier.