Deep learning approaches to anomaly detection (AD) have recently improved the state of
the art in detection performance on complex data sets, such as large collections of images or …

Abstract Out-of-distribution (OOD) detection is critical to ensuring the reliability and safety of
machine learning systems. For instance, in autonomous driving, we would like the driving …

Despite the rapid advance of unsupervised anomaly detection, existing methods require to
train separate models for different objects. In this work, we present UniAD that accomplishes …

Density-based and classification-based methods have ruled unsupervised anomaly
detection in recent years, while reconstruction-based methods are rarely mentioned for the …

Anomaly detection is an active research field in industrial defect detection and medical
disease detection. However, previous anomaly detection works suffer from unstable training …

图像异常检测是计算机视觉领域的一个热门研究课题, 其目标是在不使用真实异常样本的情况下
, 利用现有的正常样本构建模型以检测可能出现的各种异常图像, 在工业外观缺陷检测 …

The core of out-of-distribution (OOD) detection is to learn the in-distribution (ID)
representation, which is distinguishable from OOD samples. Previous work applied …

In the social production system, image data are rapidly generated from almost all fields such
as factories, hospitals, and transportation, promoting higher requirements for image anomaly …

Anomaly localization, with the purpose to segment the anomalous regions within images, is
challenging due to the large variety of anomaly types. Existing methods typically train deep …

Anomaly detection is currently an essential quality monitoring process in industrial
production. It is often affected by factors such as under or over reconstruction of images and …