Ensembled Cold-Diffusion Restorations for Unsupervised Anomaly Detection

• URL: http://arxiv.org/abs/2407.06635v1
• Date: Tue, 9 Jul 2024 08:02:46 GMT
• Title: Ensembled Cold-Diffusion Restorations for Unsupervised Anomaly Detection
• Authors: Sergio Naval Marimont, Vasilis Siomos, Matthew Baugh, Christos Tzelepis, Bernhard Kainz, Giacomo Tarroni,
• Abstract summary: Unsupervised Anomaly Detection (UAD) methods aim to identify anomalies in test samples comparing them with a normative distribution learned from a dataset known to be anomaly-free. Approaches based on generative models offer interpretability by generating anomaly-free versions of test images, but are typically unable to identify subtle anomalies. We present a novel method that combines the strengths of both strategies: a generative cold-diffusion pipeline that is trained with the objective of turning synthetically-corrupted images back to their normal, original appearance.
• Score: 7.94529540044472
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Unsupervised Anomaly Detection (UAD) methods aim to identify anomalies in test samples comparing them with a normative distribution learned from a dataset known to be anomaly-free. Approaches based on generative models offer interpretability by generating anomaly-free versions of test images, but are typically unable to identify subtle anomalies. Alternatively, approaches using feature modelling or self-supervised methods, such as the ones relying on synthetically generated anomalies, do not provide out-of-the-box interpretability. In this work, we present a novel method that combines the strengths of both strategies: a generative cold-diffusion pipeline (i.e., a diffusion-like pipeline which uses corruptions not based on noise) that is trained with the objective of turning synthetically-corrupted images back to their normal, original appearance. To support our pipeline we introduce a novel synthetic anomaly generation procedure, called DAG, and a novel anomaly score which ensembles restorations conditioned with different degrees of abnormality. Our method surpasses the prior state-of-the art for unsupervised anomaly detection in three different Brain MRI datasets.

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