OSD-IRF performs unsupervised industrial anomaly detection with a single diffusion step by evaluating anomalies in inverse residual field space under a Gaussian, delivering SOTA or competitive results with roughly 2x speedup.
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Anomaly Preference Optimization reformulates anomaly image generation as preference learning with implicit alignment from real anomalies and a time-aware capacity allocation module in diffusion models.
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One-Step Diffusion with Inverse Residual Fields for Unsupervised Industrial Anomaly Detection
OSD-IRF performs unsupervised industrial anomaly detection with a single diffusion step by evaluating anomalies in inverse residual field space under a Gaussian, delivering SOTA or competitive results with roughly 2x speedup.
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Anomaly-Preference Image Generation
Anomaly Preference Optimization reformulates anomaly image generation as preference learning with implicit alignment from real anomalies and a time-aware capacity allocation module in diffusion models.