Supervised learning algorithms have shown limited use in the field of anomaly detection due
to the unpredictability and difficulty in acquiring abnormal samples. In recent years …

The comprehensive intelligent development of the manufacturing industry puts forward new
requirements for the quality inspection of industrial products. This paper summarizes the …

Abstract Out-of-distribution (OOD) detection is vital to safety-critical machine learning
applications and has thus been extensively studied, with a plethora of methods developed in …

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 …

Visual anomaly detection is commonly used in industrial quality inspection. In this paper, we
present a new dataset as well as a new self-supervised learning method for ImageNet pre …

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 …

Abstract 2D-based Industrial Anomaly Detection has been widely discussed, however,
multimodal industrial anomaly detection based on 3D point clouds and RGB images still has …

For a long time, anomaly localization has been widely used in industries. Previous studies
focused on approximating the distribution of normal features without adaptation to a target …

High-accuracy and real-time semi-supervised image surface defect detection is extensively
needed in industrial scenarios. However, existing methods do not provide a good balance …

This paper considers few-shot anomaly detection (FSAD), a practical yet under-studied
setting for anomaly detection (AD), where only a limited number of normal images are …