Anomaly detection, finding patterns that substantially deviate from those seen previously, is
one of the fundamental problems of artificial intelligence. Recently, classification-based …

Learning representations that clearly distinguish between normal and abnormal data is key
to the success of anomaly detection. Most of existing anomaly detection algorithms use …

Anomaly detection in videos refers to identifying events that rarely or shouldn't happen in a
certain context. Among all existing methods, the idea of reconstruction or future frame …

Anomaly detection is a classical problem in computer vision, namely the determination of the
normal from the abnormal when datasets are highly biased towards one class (normal) due …

Many anomaly detection methods exist that perform well on low-dimensional problems
however there is a notable lack of effective methods for high-dimensional spaces, such as …

This paper presents a simple yet effective method for anomaly detection. The main idea is to
learn small perturbations to perturb normal data and learn a classifier to classify the 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 …

Deep generative networks trained via maximum likelihood on a natural image dataset like
CIFAR10 often assign high likelihoods to images from datasets with different objects (eg …

Recent studies give more attention to the anomaly detection (AD) methods that can leverage
a handful of labeled anomalies along with abundant unlabeled data. These existing …

Anomaly Detection (AD) in images is a fundamental computer vision problem and refers to
identifying images and/or image substructures that deviate significantly from the norm …