  We propose Gumbel Noise Score Matching (GNSM), a novel unsupervised method to
detect anomalies in categorical data. GNSM accomplishes this by estimating the
scores, i.e. the gradients of log likelihoods w.r.t.~inputs, of continuously
relaxed categorical distributions. We test our method on a suite of anomaly
detection tabular datasets. GNSM achieves a consistently high performance
across all experiments. We further demonstrate the flexibility of GNSM by
applying it to image data where the model is tasked to detect poor segmentation
predictions. Images ranked anomalous by GNSM show clear segmentation failures,
with the outputs of GNSM strongly correlating with segmentation metrics
computed on ground-truth. We outline the score matching training objective
utilized by GNSM and provide an open-source implementation of our work.
