Extended Few-Shot Learning: Exploiting Existing Resources for Novel Tasks

Understand

In many practical few-shot learning problems, even though labeled examples are scarce, there are abundant auxiliary datasets that potentially contain useful information.

• We propose the problem of extended few-shot learning to study these scenarios.
• We then introduce a framework to address the challenges of efficiently selecting and effectively using auxiliary data in few-shot image classification.
• Given a large auxiliary dataset and a notion of semantic similarity among classes, we automatically select pseudo shots, which are labeled examples from other classes related to the target task.