TabADM: Unsupervised Tabular Anomaly Detection with Diffusion Models
Guy Zamberg Moshe Salhov Ofir Lindenbaum Amir Averbuch
Tables are an abundant form of data with use cases across all scientific
fields. Real-world datasets often contain anomalous samples that can negatively
affect downstream analysis. In this work, we only assume access to contaminated
data and present a diffusion-based probabilistic model effective for
unsupervised anomaly detection. Our model is trained to learn the density of
normal samples by utilizing a unique rejection scheme to attenuate the
influence of anomalies on the density estimation. At inference, we identify
anomalies as samples in low-density regions. We use real data to demonstrate
that our method improves detection capabilities over baselines. Furthermore,
our method is relatively stable to the dimension of the data and does not
require extensive hyperparameter tuning.