Revisiting Batch Normalization for Semi-Supervised Anomaly Detection on Tabular Datasets

Batch Normalization (BN) is widely used to accelerate convergence and provide implicit regularization, yet its behavior remains largely unexplored in semi-supervised anomaly detection (SAD) for tabular data. We identify a previously overlooked failure mode, termed distribution collapse, in which the batch-to-batch variability of normalized normal representations progressively diminishes under anomaly-oversampled training. Because SAD repeatedly oversamples a limited set of labeled anomalies, their influence on shared BN statistics can make the standardized relation between normal and abnormal samples increasingly consistent across mini-batches, thereby reducing representation-level stochasticity and weakening a source of BN's implicit regularization. To address this issue while retaining the optimization benefits of BN, we propose Normal-Only Batch Normalization (NoBa), which computes normalization statistics exclusively from normal samples and removes the influence of oversampled anomalies from statistic estimation. Extensive experiments across eleven tabular datasets and five SAD models show that NoBa consistently improves upon conventional BN and alternative normalization methods while retaining BN's fast convergence.