We introduce a simple and intuitive self-supervision task, Natural Synthetic Anomalies
(NSA), for training an end-to-end model for anomaly detection and localization using only�…

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�…

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�…

Anomaly detection methods require high-quality features. In recent years, the anomaly
detection community has attempted to obtain better features using advances in deep self�…

Self-supervised feature reconstruction methods have shown promising advances in
industrial image anomaly detection and localization. Despite this progress these methods�…

Despite most existing anomaly detection studies assume the availability of normal training
samples only, a few labeled anomaly examples are often available in many real-world�…

The detection of anomalous structures in natural image data is of utmost importance for
numerous tasks in the field of computer vision. The development of methods for�…

Anomaly detection seeks to identify unusual phenomena, a central task in science and
industry. The task is inherently unsupervised as anomalies are unexpected and unknown�…

Automatic detecting anomalous regions in images of objects or textures without priors of the
anomalies is challenging, especially when the anomalies appear in very small areas of the�…

Deep autoencoder‐based methods are the majority of deep anomaly detection. An
autoencoder learning on training data is assumed to produce higher reconstruction error for�…