The problem of open-set recognition is considered. While previous approaches
only consider this problem in the context of large-scale classifier training,
we seek a unified solution for this and the low-shot classification setting. It
is argued that the classic softmax classifier is a poor solution for open-set
recognition, since it tends to overfit on the training classes. Randomization
is then proposed as a solution to this problem. This suggests the use of
meta-learning techniques, commonly used for few-shot classification, for the
solution of open-set recognition. A new oPen sEt mEta LEaRning (PEELER)
algorithm is then introduced. This combines the random selection of a set of
novel classes per episode, a loss that maximizes the posterior entropy for
examples of those classes, and a new metric learning formulation based on the
Mahalanobis distance. Experimental results show that PEELER achieves state of
the art open set recognition performance for both few-shot and large-scale
recognition. On CIFAR and miniImageNet, it achieves substantial gains in
seen/unseen class detection AUROC for a given seen-class classification
accuracy.

介绍了一种新的 oPen sEt mEta LEaRning（PEELER）算法，使用随机选择集合的新类别以及基于 Mahalanobis 距离的新度量学习方法，解决了开放集识别中的过度拟合问题，并在 few-shot 及 large-scale recognition 中达到了最先进的性能。