Deep learning (DL) techniques have recently been applied to anomaly detection
(AD), yielding successful outcomes in areas such as finance, medical services,
and cloud computing. However, much of the current research evaluates a deep AD
algorithm holistically, failing to understand the contributions of individual
design choices like loss functions and network architectures. Consequently, the
importance of prerequisite steps, such as preprocessing, might be overshadowed
by the spotlight on novel loss functions and architectures. In this paper, we
address these oversights by posing two questions: (i) Which components (i.e.,
design choices) of deep AD methods are pivotal in detecting anomalies? (ii) How
can we construct tailored AD algorithms for specific datasets by selecting the
best design choices automatically, rather than relying on generic, pre-existing
solutions? To this end, we introduce ADGym, the first platform designed for
comprehensive evaluation and automatic selection of AD design elements in deep
methods. Extensive experiments reveal that merely adopting existing leading
methods is not ideal. Models crafted using ADGym markedly surpass current
state-of-the-art techniques.

该论文介绍了 ADGym，这是一个专为深度方法中的异常检测设计元素进行全面评估和自动选择而设计的平台。通过广泛的实验，表明仅仅采用现有的先进方法并不理想，使用 ADGym 构建的模型显著超越目前最先进的技术。