  Surface anomaly detection is a vital component in manufacturing inspection.
Current discriminative methods follow a two-stage architecture composed of a
reconstructive network followed by a discriminative network that relies on the
reconstruction output. Currently used reconstructive networks often produce
poor reconstructions that either still contain anomalies or lack details in
anomaly-free regions. Discriminative methods are robust to some reconstructive
network failures, suggesting that the discriminative network learns a strong
normal appearance signal that the reconstructive networks miss. We reformulate
the two-stage architecture into a single-stage iterative process that allows
the exchange of information between the reconstruction and localization. We
propose a novel transparency-based diffusion process where the transparency of
anomalous regions is progressively increased, restoring their normal appearance
accurately while maintaining the appearance of anomaly-free regions using
localization cues of previous steps. We implement the proposed process as
TRANSparency DifFUSION (TransFusion), a novel discriminative anomaly detection
method that achieves state-of-the-art performance on both the VisA and the
MVTec AD datasets, with an image-level AUROC of 98.5% and 99.2%, respectively.
Code: https://github.com/MaticFuc/ECCV_TransFusion
