论文标题
Adbench：异常检测基准测试
ADBench: Anomaly Detection Benchmark
论文作者
论文摘要
考虑到过去几十年中开发的一长串异常检测算法，它们如何在（i）（i）不同级别的监督，（ii）不同类型的异常以及（iii）嘈杂和损坏的数据方面执行？在这项工作中，我们通过（据我们所知）在57个名为Adbench的57个基准数据集上使用30个算法来回答这些关键问题。我们的广泛实验（总共98,436个）确定了对监督和异常类型的作用的有意义的见解，并解锁了研究人员在算法选择和设计中的未来方向。借助Adbench，研究人员可以轻松地对现有基线的数据集（包括我们从自然语言和计算机视觉领域的贡献）上对新提出的方法进行全面和公平的评估。为了促进可访问性和可重复性，我们完全开源的Adbench和相应的结果。
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.
