Out-of-distribution (OOD) detection has received much attention lately due to
its importance in the safe deployment of neural networks. One of the key
challenges is that models lack supervision signals from unknown data, and as a
result, can produce overconfident predictions on OOD data. Previous approaches
rely on real outlier datasets for model regularization, which can be costly and
sometimes infeasible to obtain in practice. In this paper, we present VOS, a
novel framework for OOD detection by adaptively synthesizing virtual outliers
that can meaningfully regularize the model's decision boundary during training.
Specifically, VOS samples virtual outliers from the low-likelihood region of
the class-conditional distribution estimated in the feature space. Alongside,
we introduce a novel unknown-aware training objective, which contrastively
shapes the uncertainty space between the ID data and synthesized outlier data.
VOS achieves competitive performance on both object detection and image
classification models, reducing the FPR95 by up to 9.36% compared to the
previous best method on object detectors. Code is available at
this https URL

VOS 是一种新的框架，用于通过自适应合成虚拟离群值，在训练期间能够有意义地规范模型的决策边界，包括在特征空间中从类条件分布进行虚拟离群值取样，同时引入一种新的未知感知训练目标，与 ID 数据和合成离群数据之间的不确定性空间进行反向塑形，VOS 与物体检测和图像分类模型均能实现竞争性表现，在物体检测器上将 FPR95 降低了最高达 9.36％。