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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2406.14398 |
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| _version_ | 1866917793472970752 |
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| author | Gupta, Shaurya Gautam, Neil Malyala, Anurag |
| author_facet | Gupta, Shaurya Gautam, Neil Malyala, Anurag |
| contents | The application of deep learning in visual anomaly detection has gained widespread popularity due to its potential use in quality control and manufacturing. Current standard methods are Unsupervised, where a clean dataset is utilised to detect deviations and flag anomalies during testing. However, incorporating a few samples when the type of anomalies is known beforehand can significantly enhance performance. Thus, we propose ATAC-Net, a framework that trains to detect anomalies from a minimal set of known prior anomalies. Furthermore, we introduce attention-guided cropping, which provides a closer view of suspect regions during the training phase. Our framework is a reliable and easy-to-understand system for detecting anomalies, and we substantiate its superiority to some of the current state-of-the-art techniques in a comparable setting. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_14398 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | ATAC-Net: Zoomed view works better for Anomaly Detection Gupta, Shaurya Gautam, Neil Malyala, Anurag Computer Vision and Pattern Recognition The application of deep learning in visual anomaly detection has gained widespread popularity due to its potential use in quality control and manufacturing. Current standard methods are Unsupervised, where a clean dataset is utilised to detect deviations and flag anomalies during testing. However, incorporating a few samples when the type of anomalies is known beforehand can significantly enhance performance. Thus, we propose ATAC-Net, a framework that trains to detect anomalies from a minimal set of known prior anomalies. Furthermore, we introduce attention-guided cropping, which provides a closer view of suspect regions during the training phase. Our framework is a reliable and easy-to-understand system for detecting anomalies, and we substantiate its superiority to some of the current state-of-the-art techniques in a comparable setting. |
| title | ATAC-Net: Zoomed view works better for Anomaly Detection |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2406.14398 |