Supervised learning algorithms have shown limited use in the field of anomaly detection due
to the unpredictability and difficulty in acquiring abnormal samples. In recent years …

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 …

Unsupervised representation learning has proved to be a critical component of anomaly
detection/localization in images. The challenges to learn such a representation are two-fold …

Anomaly detection (AD) plays a crucial role in various domains, including cybersecurity,
finance, and healthcare, by identifying patterns or events that deviate from normal behavior …

Machine learning models often encounter samples that are diverged from the training
distribution. Failure to recognize an out-of-distribution (OOD) sample, and consequently …

Density-based and classification-based methods have ruled unsupervised anomaly
detection in recent years, while reconstruction-based methods are rarely mentioned for the …

Anomaly detection is an active research field in industrial defect detection and medical
disease detection. However, previous anomaly detection works suffer from unstable training …

Self-supervised learning approaches leverage unlabeled samples to acquire generic
knowledge about different concepts, hence allowing for annotation-efficient downstream …

Deep autoencoder (AE) has demonstrated promising performances in visual anomaly
detection (VAD). Learning normal patterns on normal data, deep AE is expected to yield …

Over the past few years, anomaly detection, a subfield of machine learning that is mainly
concerned with the detection of rare events, witnessed an immense improvement following …