Few-Shot Open-Set Recognition using Meta-Learning
- URL: http://arxiv.org/abs/2005.13713v2
- Date: Sun, 7 Jun 2020 19:15:41 GMT
- Title: Few-Shot Open-Set Recognition using Meta-Learning
- Authors: Bo Liu, Hao Kang, Haoxiang Li, Gang Hua, Nuno Vasconcelos
- Abstract summary: The problem of open-set recognition is considered.
A new oPen sEt mEta LEaRning (PEELER) algorithm is introduced.
- Score: 72.15940446408824
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: 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.
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