  Most approaches to dense anomaly detection rely on generative modeling or on
discriminative methods that train with negative data. We consider a recent
hybrid method that optimizes the same shared representation according to
cross-entropy of the discriminative predictions, and negative log likelihood of
the predicted energy-based density. We extend that work with a jointly trained
generative flow that samples synthetic negatives at the border of the inlier
distribution. The proposed extension provides potential to learn the hybrid
method without real negative data. Our experiments analyze the impact of
training with synthetic negative data and validate contribution of the
energy-based density during training and evaluation.
