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 …

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 …

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

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

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 …

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 …

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 …

Deep autoencoder has been extensively used for anomaly detection. Training on the normal
data, the autoencoder is expected to produce higher reconstruction error for the abnormal …

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 …

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 …