  Deep learning (DL) techniques have recently found success in anomaly
detection (AD) across various fields such as finance, medical services, and
cloud computing. However, most of the current research tends to view deep AD
algorithms as a whole, without dissecting the contributions of individual
design choices like loss functions and network architectures. This view tends
to diminish the value of preliminary steps like data preprocessing, as more
attention is given to newly designed loss functions, network architectures, and
learning paradigms. In this paper, we aim to bridge this gap by asking two key
questions: (i) Which design choices in deep AD methods are crucial for
detecting anomalies? (ii) How can we automatically select the optimal design
choices for a given AD dataset, instead of relying on generic, pre-existing
solutions? To address these questions, we introduce ADGym, a platform
specifically crafted for comprehensive evaluation and automatic selection of AD
design elements in deep methods. Our extensive experiments reveal that relying
solely on existing leading methods is not sufficient. In contrast, models
developed using ADGym significantly surpass current state-of-the-art
techniques.
