FirstDiff models the statistical distribution of predicted diffusion noise under normal behavior using validation data, enabling anomaly inference from a single denoising-network evaluation rather than completing the reverse diffusion trajectory.
Unsupervised anomaly detection (UAD) is crucial in industrial applications, and condition-guided denoising diffusion models (DDMs) have attracted increasing attention because of their strong reconstruction capabilities. However, existing guidance conditions do not effectively decouple the reconstruction requirements of...
Accurate anomalous localization is a core challenge in industrial visual quality inspection. Current diffusion-based methods typically rely on single-scale reconstruction at a fixed resolution, often exhibiting limited performance on subtle or low-contrast anomalies due to the lack of hierarchical feature collaboration...
Anomaly detection in noisy images is essential for quality control in industries like steel, composites, and textiles. Traditional unsupervised anomaly segmentation models, including Robust Principal Component Analysis and Smooth Sparse Decomposition, rely on restrictive data assumptions including anomaly sparsity and...
Robust anomaly detection in time series remains challenging because sparse abnormal observations, noise contamination, nonlinear dynamics, and long-range temporal dependencies can obscure deviation patterns. This paper proposes the SALK anomaly detection model, which integrates an attention-enhanced long short-term mem...
Multivariate time series anomaly detection (MTAD) is important for ensuring reliable operation and improving service quality in industrial systems. Forecasting-based methods have been a primary approach for MTAD, detecting anomalies by assuming that anomalous data points produce higher forecasting errors than normal on...
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