Anomaly Detection with Conditioned Denoising Diffusion Models
This addresses the challenge of improving anomaly detection performance for image analysis, though it appears incremental as it builds on existing denoising diffusion models with conditioning.
The paper tackled the problem of anomaly detection in images by introducing Denoising Diffusion Anomaly Detection (DDAD), a method using conditioned denoising diffusion models for reconstruction, achieving state-of-the-art results of 99.8% and 98.9% image-level AUROC on MVTec and VisA benchmarks.
Traditional reconstruction-based methods have struggled to achieve competitive performance in anomaly detection. In this paper, we introduce Denoising Diffusion Anomaly Detection (DDAD), a novel denoising process for image reconstruction conditioned on a target image. This ensures a coherent restoration that closely resembles the target image. Our anomaly detection framework employs the conditioning mechanism, where the target image is set as the input image to guide the denoising process, leading to a defectless reconstruction while maintaining nominal patterns. Anomalies are then localised via a pixel-wise and feature-wise comparison of the input and reconstructed image. Finally, to enhance the effectiveness of the feature-wise comparison, we introduce a domain adaptation method that utilises nearly identical generated examples from our conditioned denoising process to fine-tune the pretrained feature extractor. The veracity of DDAD is demonstrated on various datasets including MVTec and VisA benchmarks, achieving state-of-the-art results of \(99.8 \%\) and \(98.9 \%\) image-level AUROC respectively.