Unsupervised texture anomaly detection has been a concerning topic in a vast number of
industrial processes. Patterned textures inspection, particularly in the context of fabric defect …

Medical data have unique specificity and professionalism, requiring substantial domain
expertise for their annotation. Precise data annotation is essential for anomaly-detection …

One persistent obstacle in industrial quality inspection is the detection of anomalies. In real-
world use cases, two problems must be addressed: anomalous data is sparse and the same …

Anomaly detection is a fundamental and challenging task in computer vision, which
determines whether an image contains anomaly or not. Prior works using autoencoders for …

Recent anomaly detection methods achieve high performance on commonly used image
and pixel-level metrics. However, due to the imbalance in the number of normal and …

Deep-learning based approaches for unsupervised anomaly detection typically learn either
a generative model of the inlier class or a decision boundary to encapsulate the inlier class …