ADBench: Anomaly Detection Benchmark
This work addresses the need for standardized evaluation in anomaly detection research, enabling fair comparisons and guiding future algorithm development.
The authors tackled the problem of evaluating anomaly detection algorithms by creating ADBench, a comprehensive benchmark with 30 algorithms on 57 datasets, conducting 98,436 experiments to provide insights into supervision and anomaly types.
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, and (iii) noisy and corrupted data? In this work, we answer these key questions by conducting (to our best knowledge) the most comprehensive anomaly detection benchmark with 30 algorithms on 57 benchmark datasets, named ADBench. Our extensive experiments (98,436 in total) identify meaningful insights into the role of supervision and anomaly types, and unlock future directions for researchers in algorithm selection and design. With ADBench, researchers can easily conduct comprehensive and fair evaluations for newly proposed methods on the datasets (including our contributed ones from natural language and computer vision domains) against the existing baselines. To foster accessibility and reproducibility, we fully open-source ADBench and the corresponding results.