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Due to the unsupervised nature of anomaly detection, the key to fueling deepmodels is finding supervisory signals. Different from currentreconstruction-guided generative models and transformation-based contrastivemodels, we devise novel data-driven supervision for tabular data by introducinga characteristic -- scale -- as data labels. By representing varied sub-vectorsof data instances, we define scale as the relationship between thedimensionality of original sub-vectors and that of representations. Scalesserve as labels attached to transformed representations, thus offering amplelabeled data for neural network training. This paper further proposes a scalelearning-based anomaly detection method. Supervised by the learning objectiveof scale distribution alignment, our approach learns the ranking ofrepresentations converted from varied subspaces of each data instance. Throughthis proxy task, our approach models inherent regularities and patterns withindata, which well describes data "normality". Abnormal degrees of testinginstances are obtained by measuring whether they fit these learned patterns.Extensive experiments show that our approach leads to significant improvementover state-of-the-art generative/contrastive anomaly detection methods.

Fascinating Supervisory Signals and Where to Find Them: Deep Anomaly Detection with Scale Learning