Visual anomaly detection, an important problem in computer vision, is usually formulated as
a one-class classification and segmentation task. The student-teacher (ST) framework has …

Anomaly detection is a challenging task and usually formulated as an one-class learning
problem for the unexpectedness of anomalies. This paper proposes a simple yet powerful …

Detecting anomalies in images is an important task, especially in real-time computer vision
applications. In this work, we focus on computational efficiency and propose a lightweight …

We introduce a powerful student-teacher framework for the challenging problem of
unsupervised anomaly detection and pixel-precise anomaly segmentation in high-resolution …

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 vision, the anomaly detection problem can be addressed with an autoencoder
trained to map an arbitrary image, ie with or without any defect, to a clean image, ie without …

Anomaly detection is an important problem in computer vision; however, the scarcity of
anomalous samples makes this task difficult. Thus, recent anomaly detection methods have …

Most anomaly detection (AD) models are learned using only normal samples in an
unsupervised way, which may result in ambiguous decision boundary and insufficient …

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 …

We address the problem of anomaly detection, that is, detecting anomalous events in a
video sequence. Anomaly detection methods based on convolutional neural networks …