Self-supervised learning for time series analysis: Taxonomy, progress, and prospects
Self-supervised learning (SSL) has recently achieved impressive performance on various
time series tasks. The most prominent advantage of SSL is that it reduces the dependence …
time series tasks. The most prominent advantage of SSL is that it reduces the dependence …
Latent outlier exposure for anomaly detection with contaminated data
Anomaly detection aims at identifying data points that show systematic deviations from the
majority of data in an unlabeled dataset. A common assumption is that clean training data …
majority of data in an unlabeled dataset. A common assumption is that clean training data …
Deep anomaly detection under labeling budget constraints
Selecting informative data points for expert feedback can significantly improve the
performance of anomaly detection (AD) in various contexts, such as medical diagnostics or …
performance of anomaly detection (AD) in various contexts, such as medical diagnostics or …
Zero-shot anomaly detection via batch normalization
Anomaly detection (AD) plays a crucial role in many safety-critical application domains. The
challenge of adapting an anomaly detector to drift in the normal data distribution, especially …
challenge of adapting an anomaly detector to drift in the normal data distribution, especially …
Can industrial intrusion detection be simple?
Cyberattacks against industrial control systems pose a serious risk to the safety of humans
and the environment. Industrial intrusion detection systems oppose this threat by …
and the environment. Industrial intrusion detection systems oppose this threat by …
EGNN: Energy-efficient anomaly detection for IoT multivariate time series data using graph neural network
Anomaly detection has been widely applied in Internet of Things (IoT) to guarantee the
health of IoT applications. Current studies on anomaly detection focus mainly on …
health of IoT applications. Current studies on anomaly detection focus mainly on …
Model Selection of Anomaly Detectors in the Absence of Labeled Validation Data
Anomaly detection requires detecting abnormal samples in large unlabeled datasets. While
progress in deep learning and the advent of foundation models has produced powerful …
progress in deep learning and the advent of foundation models has produced powerful …
Self-supervised Spatial-Temporal Normality Learning for Time Series Anomaly Detection
Abstract Time Series Anomaly Detection (TSAD) finds widespread applications across
various domains such as financial markets, industrial production, and healthcare. Its primary …
various domains such as financial markets, industrial production, and healthcare. Its primary …
Self-Supervised Anomaly Detection with Neural Transformations
Data augmentation plays a critical role in self-supervised learning, including anomaly
detection. While hand-crafted transformations such as image rotations can achieve …
detection. While hand-crafted transformations such as image rotations can achieve …
A generic machine learning framework for fully-unsupervised anomaly detection with contaminated data
M Ulmer, J Zgraggen, LG Huber - arXiv preprint arXiv:2308.13352, 2023 - arxiv.org
Anomaly detection (AD) tasks have been solved using machine learning algorithms in
various domains and applications. The great majority of these algorithms use normal data to …
various domains and applications. The great majority of these algorithms use normal data to …