Deep industrial image anomaly detection: A survey
The recent rapid development of deep learning has laid a milestone in industrial image
anomaly detection (IAD). In this paper, we provide a comprehensive review of deep learning …
anomaly detection (IAD). In this paper, we provide a comprehensive review of deep learning …
Adbench: Anomaly detection benchmark
Given a long list of anomaly detection algorithms developed in the last few decades, how do
they perform with regard to (i) varying levels of supervision,(ii) different types of anomalies …
they perform with regard to (i) varying levels of supervision,(ii) different types of anomalies …
Softpatch: Unsupervised anomaly detection with noisy data
Although mainstream unsupervised anomaly detection (AD) algorithms perform well in
academic datasets, their performance is limited in practical application due to the ideal …
academic datasets, their performance is limited in practical application due to the ideal …
Fascinating supervisory signals and where to find them: Deep anomaly detection with scale learning
Due to the unsupervised nature of anomaly detection, the key to fueling deep models is
finding supervisory signals. Different from current reconstruction-guided generative models …
finding supervisory signals. Different from current reconstruction-guided generative models …
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 …
Calibrated one-class classification for unsupervised time series anomaly detection
Time series anomaly detection is instrumental in maintaining system availability in various
domains. Current work in this research line mainly focuses on learning data normality …
domains. Current work in this research line mainly focuses on learning data normality …
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 …
Unilaterally aggregated contrastive learning with hierarchical augmentation for anomaly detection
Anomaly detection (AD), aiming to find samples that deviate from the training distribution, is
essential in safety-critical applications. Though recent self-supervised learning based …
essential in safety-critical applications. Though recent self-supervised learning based …
Hierarchical semi-supervised contrastive learning for contamination-resistant anomaly detection
Anomaly detection aims at identifying deviant samples from the normal data distribution.
Contrastive learning has provided a successful way to sample representation that enables …
Contrastive learning has provided a successful way to sample representation that enables …
A filter-augmented auto-encoder with learnable normalization for robust multivariate time series anomaly detection
J Yu, X Gao, B Li, F Zhai, J Lu, B Xue, S Fu, C **ao - Neural Networks, 2024 - Elsevier
While existing reconstruction-based multivariate time series (MTS) anomaly detection
methods demonstrate advanced performance on many challenging real-world datasets, they …
methods demonstrate advanced performance on many challenging real-world datasets, they …