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 …

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 …

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 …

Being able to spot defective parts is a critical component in large-scale industrial
manufacturing. A particular challenge that we address in this work is the cold-start problem …

We aim at constructing a high performance model for defect detection that detects unknown
anomalous patterns of an image without anomalous data. To this end, we propose a two …

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

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 …

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 …

In industrial manufacturing processes, errors frequently occur at unpredictable times and in
unknown manifestations. We tackle this problem, known as automatic defect detection …