Currently, deep learning-based visual inspection has been highly successful with the help of
supervised learning methods. However, in real industrial scenarios, the scarcity of defect …

The recent rapid development of deep learning has laid a milestone in industrial image
anomaly detection (IAD). In this paper, we provide a comprehensive review of deep learning …

Abstract Knowledge distillation (KD) achieves promising results on the challenging problem
of unsupervised anomaly detection (AD). The representation discrepancy of anomalies in …

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 …

We propose a simple and application-friendly network (called SimpleNet) for detecting and
localizing anomalies. SimpleNet consists of four components:(1) a pre-trained Feature …

Unsupervised anomaly detection with localization has many practical applications when
labeling is infeasible and, moreover, when anomaly examples are completely missing in the …

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 …

Visual anomaly classification and segmentation are vital for automating industrial quality
inspection. The focus of prior research in the field has been on training custom models for …

Anomaly detection is commonly pursued as a one-class classification problem, where
models can only learn from normal training samples, while being evaluated on both normal …

Machine learning models often encounter samples that are diverged from the training
distribution. Failure to recognize an out-of-distribution (OOD) sample, and consequently …