By design, discriminatively trained neural network classifiers produce
reliable predictions only for in-distribution samples. For their real-world
deployments, detecting out-of-distribution (OOD) samples is essential. Assuming
OOD to be outside the closed boundary of in-distribution, typical neural
classifiers do not contain the knowledge of this boundary for OOD detection
during inference. There have been recent approaches to instill this knowledge
in classifiers by explicitly training the classifier with OOD samples close to
the in-distribution boundary. However, these generated samples fail to cover
the entire in-distribution boundary effectively, thereby resulting in a
sub-optimal OOD detector. In this paper, we analyze the feasibility of such
approaches by investigating the complexity of producing such "effective" OOD
samples. We also propose a novel algorithm to generate such samples using a
manifold learning network (e.g., variational autoencoder) and then train an n+1
classifier for OOD detection, where the $n+1^{th}$ class represents the OOD
samples. We compare our approach against several recent classifier-based OOD
detectors on MNIST and Fashion-MNIST datasets. Overall the proposed approach
consistently performs better than the others.

本文中，我们通过研究生成 “有效” 的 OOD 样本的复杂性，分析了调整之前的方法的可行性。我们提出了一种使用流形学习网络（例如变分自编码器）生成这些样本的新算法，然后训练一个 $n+1$ 分类器来检测 OOD，其中第 $n+1$ 类代表 OOD 样本。我们在 MNIST 和 Fashion-MNIST 数据集上将我们的方法与几种最近的基于分类器的 OOD 检测器进行了比较，实验表明我们的方法总体上表现更好。