TransFusion -- A Transparency-Based Diffusion Model for Anomaly Detection

• URL: http://arxiv.org/abs/2311.09999v2
• Date: Wed, 10 Jul 2024 13:44:42 GMT
• Title: TransFusion -- A Transparency-Based Diffusion Model for Anomaly Detection
• Authors: Matic Fučka, Vitjan Zavrtanik, Danijel Skočaj,
• Abstract summary: We propose a novel discriminative anomaly detection method that achieves state-of-the-art performance on two datasets. TransFusion 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.
• Score: 2.7855886538423182
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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

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