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.
European conference on computer vision , pages=
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
fields
cs.CV 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
Anomaly Preference Optimization reformulates anomaly image generation as preference learning using real anomalies for implicit alignment signals from denoising trajectories plus a time-aware capacity allocation module.
citing papers explorer
-
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.
-
Anomaly-Preference Image Generation
Anomaly Preference Optimization reformulates anomaly image generation as preference learning using real anomalies for implicit alignment signals from denoising trajectories plus a time-aware capacity allocation module.