One Representation to Rule Them All: Identifying Out-of-Support Examples in Few-shot Learning with Generic Representations

Abstract

The field of few-shot learning has made remarkable strides in developing powerful models that can operate in the small data regime. Nearly all of these methods assume every unlabeled instance encountered will belong to a handful of known classes for which one has examples. This can be problematic for real-world use cases where one routinely finds 'none-of-the-above' examples. In this paper we describe this challenge of identifying what we term 'out-of-support' (OOS) examples. We describe how this problem is subtly different from out-of-distribution detection and describe a new method of identifying OOS examples within the Prototypical Networks framework using a fixed point which we call the generic representation. We show that our method outperforms other existing approaches in the literature as well as other approaches that we propose in this paper. Finally, we investigate how the use of such a generic point affects the geometry of a model's feature space.

arXiv comment15 pages

References in corpus (6)

• Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks
• Prototypical Networks for Few-shot Learning
• A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networks
• Fine-Grained Visual Classification of Aircraft
• Generalizing from a Few Examples: A Survey on Few-Shot Learning
• A Closer Look at Few-shot Classification