Anomaly Detection is an unsupervised learning task aimed at detecting anomalous
behaviors with respect to historical data. In particular, multivariate Anomaly Detection has an …

Isolation forest (iForest) has been emerging as arguably the most popular anomaly detector
in recent years due to its general effectiveness across different benchmarks and strong …

With complexity of artificial intelligence systems increasing continuously in past years,
studies to explain these complex systems have grown in popularity. While much work has …

In the past recent years, Machine Learning methodologies have been applied in countless
application areas. In particular, they play a key role in enabling Industry 4.0. However, one of …

In the past two decades, most research on anomaly detection has focused on improving the
accuracy of the detection, while largely ignoring the explainability of the corresponding …

In this paper, we consider the problem of anomaly detection. Previous studies mostly deal
with this task in either supervised or unsupervised manner according to whether label …

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 …

Deep learning (DL) techniques have recently found success in anomaly detection (AD)
across various fields such as finance, medical services, and cloud computing. However …

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

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 …