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Unsupervised Anomaly Detection Using Diffusion Trend Analysis for Display Inspection

arXiv 2407.09578 ↗First posted 12 Jul 2024cs.CV cs.LGcited by 0

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Abstract (arXiv)

Reconstruction-based anomaly detection via denoising diffusion model has limitations in determining appropriate noise parameters that can degrade anomalies while preserving normal characteristics. Also, normal regions can fluctuate considerably during reconstruction, resulting in false detection. In this paper, we propose a method to detect anomalies by analysis of reconstruction trend depending on the degree of degradation, effectively solving the both problems that impede practical application in display inspection.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 7 of its 11 references that are in the map. First posted on arXiv 12 Jul 2024.

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7 of 11 references.

References · 7

  1. 2020Denoising Diffusion Probabilistic Modelscited by 20,851
  2. 2020Denoising Diffusion Implicit Modelscited by 9,655
  3. 2020Score-Based Generative Modeling through Stochastic Differential Equationscited by 8,964
  4. 2021Diffusion Models Beat GANs on Image Synthesiscited by 8,185
  5. 2023Anomaly Detection with Conditioned Denoising Diffusion Modelscited by 56
  6. 2022A Survey on Unsupervised Anomaly Detection Algorithms for Industrial Imagescited by 21
  7. 2020Interpreting Rate-Distortion of Variational Autoencoder and Using Model Uncertainty for Anomaly Detectioncited by 10

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  1. 2025Correcting Deviations from Normality: A Reformulated Diffusion Model for Multi-Class Unsupervised Anomaly Detection
  2. 2023DiffusionAD: Norm-guided One-step Denoising Diffusion for Anomaly Detection
  3. 2024GLAD: Towards Better Reconstruction with Global and Local Adaptive Diffusion Models for Unsupervised Anomaly Detection
  4. 2025InvAD: Inversion-based Reconstruction-Free Anomaly Detection with Diffusion Models
  5. 2024Unsupervised Anomaly Detection via Masked Diffusion Posterior Sampling
  6. 2025Single-Step Reconstruction-Free Anomaly Detection and Segmentation via Diffusion Models
  7. 2024Enhancing Multi-Class Anomaly Detection via Diffusion Refinement with Dual Conditioning
  8. 2022Unsupervised Visual Defect Detection with Score-Based Generative Model