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

Real time dense anomaly detection by learning on synthetic negative data