TOD: GPU-accelerated Outlier Detection via Tensor Operations

The Need for Unsupervised Outlier Model Selection: A Review and Evaluation of Internal Evaluation Strategies

ADBench: Anomaly Detection Benchmark

BOND: Benchmarking Unsupervised Outlier Node Detection on Static Attributed Graphs

ELECT: Toward Unsupervised Outlier Model Selection

ECOD: Unsupervised Outlier Detection Using Empirical Cumulative Distribution Functions

Automatic Unsupervised Outlier Model Selection

Revisiting Time Series Outlier Detection: Definitions and Benchmarks

SUOD: Accelerating Large-scale Unsupervised Heterogeneous Outlier Detection

Outlier detection (OD) is a key data mining task for identifying abnormal objects from general samples with numerous high-stake applications including fraud detection and intrusion detection. Due to the lack of ground truth labels, practitioners …

TODS: An Automated Time Series Outlier Detection System