论文标题
使用元学习的几个开放式识别
Few-Shot Open-Set Recognition using Meta-Learning
论文作者
论文摘要
考虑开放式识别的问题。尽管以前的方法仅在大规模分类器培训的背景下考虑这个问题，但我们为此寻求统一的解决方案和低射击分类设置。有人认为，经典的软马克斯分类器是开放式识别的糟糕解决方案，因为它倾向于在培训课程上过度贴上。然后提出随机化作为解决此问题的解决方案。这表明使用元学习技术（通常用于几杆分类）用于开放式识别。然后引入了一种新的开放式元学习（Peeler）算法。这结合了每个情节的一组新颖类的随机选择，这是这些类别示例的后熵最大化的损失，以及基于Mahalanobis距离的新公制学习公式。实验结果表明，Peeler可在几次识别和大规模识别方面达到最新的开放式识别性能。在CIFAR和MINIIMAGENET上，它在给定的见面类别的准确性方面可实现可见/看不见的类检测AUROC。
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.
