Unsupervised Anomaly Detection Using Diffusion Trend Analysis for Display Inspection
Fuente:
arXiv
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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| Soggetti: | |
| Accesso online: | |
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| _version_ | 1866913996703006720 |
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| author | Kim, Eunwoo Yang, Un Roh, Cheol Lae Ermon, Stefano |
| author_facet | Kim, Eunwoo Yang, Un Roh, Cheol Lae Ermon, Stefano |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_09578 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Unsupervised Anomaly Detection Using Diffusion Trend Analysis for Display Inspection Kim, Eunwoo Yang, Un Roh, Cheol Lae Ermon, Stefano Computer Vision and Pattern Recognition Machine Learning 68T45 (Primary) 68T27 (Secondary) I.2.10 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. |
| title | Unsupervised Anomaly Detection Using Diffusion Trend Analysis for Display Inspection |
| topic | Computer Vision and Pattern Recognition Machine Learning 68T45 (Primary) 68T27 (Secondary) I.2.10 |
| url | https://arxiv.org/abs/2407.09578 |