  Anomaly detection in visual data refers to the problem of differentiating
abnormal appearances from normal cases. Supervised approaches have been
successfully applied to different domains, but require an abundance of labeled
data. Due to the nature of how anomalies occur and their underlying generating
processes, it is hard to characterize and label them. Recent advances in deep
generative-based models have sparked interest in applying such methods for
unsupervised anomaly detection and have shown promising results in medical and
industrial inspection domains. In this work we evaluate a crucial part of the
unsupervised visual anomaly detection pipeline, that is needed for normal
appearance modeling, as well as the ability to reconstruct closest looking
normal and tumor samples. We adapt and evaluate different high-resolution
state-of-the-art generative models from the face synthesis domain and
demonstrate their superiority over currently used approaches on a challenging
domain of digital pathology. Multifold improvement in image synthesis is
demonstrated in terms of the quality and resolution of the generated images,
validated also against the supervised model.
