  Accurate and automated detection of anomalous samples in a natural image
dataset can be accomplished with a probabilistic model for end-to-end modeling
of images. Such images have heterogeneous complexity, however, and a
probabilistic model overlooks simply shaped objects with small anomalies. This
is because the probabilistic model assigns undesirably lower likelihoods to
complexly shaped objects that are nevertheless consistent with set standards.
To overcome this difficulty, we propose an unregularized score for deep
generative models (DGMs), which are generative models leveraging deep neural
networks. We found that the regularization terms of the DGMs considerably
influence the anomaly score depending on the complexity of the samples. By
removing these terms, we obtain an unregularized score, which we evaluated on a
toy dataset and real-world manufacturing datasets. Empirical results
demonstrate that the unregularized score is robust to the inherent complexity
of samples and can be used to better detect anomalies.
