Yadang Chen
ABSTRACT
Anomaly detection refers to identifying abnormal images and localizing anomalous regions. Reconstruction-based anomaly detection is a commonly used method; however, traditional reconstruction-based methods perform poorly as deep models generalize successfully enough that even anomalous regions can be well-restored. In this paper, we propose a new method to address the single pseudo-anomaly type and high false positive detection of the existing methods. Specifically, we design a novel pseudo-anomaly simulation module that can generate several types of anomalies on normal images. Furthermore, we propose an effective reconstruction network to improve the robustness of the model against distractors. Finally, we employ a segmentation network to localize anomalous regions. This simple but effective method can detect various anomalies in the real world, even those that are subtle and rare. Extensive experiments on the MVTec anomaly detection dataset demonstrate the effectiveness and superiority of the proposed method, yielding an AUROC score of 98.2% in image-level anomaly detection and 97.8% in pixel-level anomaly localization.
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- Samet Akcay, Amir Atapour-Abarghouei, and Toby P Breckon. 2018. Ganomaly: Semi-supervised anomaly detection via adversarial training. In Asian Conference on Computer Vision. Springer, Cham, 622–637.Google Scholar
- Samet Akçay, Amir Atapour-Abarghouei, and Toby P Breckon. 2019. Skip-ganomaly: Skip connected and adversarially trained encoder-decoder anomaly detection. In 2019 International Joint Conference on Neural Networks (IJCNN). IEEE, Piscataway, NJ, 1–8.Google ScholarCross Ref
- Laura Beggel, Michael Pfeiffer, and Bernd Bischl. 2019. Robust anomaly detection in images using adversarial autoencoders. In Joint European Conference on Machine Learning and Knowledge Discovery in Databases. Springer, Cham, 206–222.Google Scholar
- Paul Bergmann, Michael Fauser, David Sattlegger, and Carsten Steger. 2019. MVTec AD — A Comprehensive Real-World Dataset for Unsupervised Anomaly Detection. In 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, Piscataway, NJ, 9584–9592.Google ScholarCross Ref
- Paul Bergmann, Michael Fauser, David Sattlegger, and Carsten Steger. 2020. Uninformed students: Student-teacher anomaly detection with discriminative latent embeddings. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. IEEE, Piscataway, NJ, 4183–4192.Google ScholarCross Ref
- Paul Bergmann., Sindy Löwe., Michael Fauser., David Sattlegger., and Carsten Steger.2019. Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoders. In Proceedings of the 14th International Joint Conference on Computer Vision. SciTePress, Setúbal, Portugal, 372–380.Google Scholar
- Mircea Cimpoi, Subhransu Maji, Iasonas Kokkinos, Sammy Mohamed, and Andrea Vedaldi. 2014. Describing Textures in the Wild. In 2014 IEEE Conference on Computer Vision and Pattern Recognition. IEEE, Piscataway, NJ, 3606–3613.Google ScholarDigital Library
- Anne-Sophie Collin and Christophe De Vleeschouwer. 2021. Improved anomaly detection by training an autoencoder with skip connections on images corrupted with stain-shaped noise. In 2020 25th International Conference on Pattern Recognition (ICPR). IEEE, Piscataway, NJ, 7915–7922.Google ScholarCross Ref
- Thomas Defard, Aleksandr Setkov, Angelique Loesch, and Romaric Audigier. 2021. Padim: a patch distribution modeling framework for anomaly detection and localization. In International Conference on Pattern Recognition. Springer, Cham, 475–489.Google ScholarDigital Library
- Dong Gong, Lingqiao Liu, Vuong Le, Budhaditya Saha, Moussa Reda Mansour, Svetha Venkatesh, and Anton van den Hengel. 2019. Memorizing normality to detect anomaly: Memory-augmented deep autoencoder for unsupervised anomaly detection. In Proceedings of the IEEE/CVF International Conference on Computer Vision. IEEE, Piscataway, NJ, 1705–1714.Google ScholarCross Ref
- Jinlei Hou, Yingying Zhang, Qiaoyong Zhong, Di Xie, Shiliang Pu, and Hong Zhou. 2021. Divide-and-assemble: Learning block-wise memory for unsupervised anomaly detection. In Proceedings of the IEEE/CVF International Conference on Computer Vision. IEEE, Piscataway, NJ, 8791–8800.Google ScholarCross Ref
- Chun-Liang Li, Kihyuk Sohn, Jinsung Yoon, and Tomas Pfister. 2021. CutPaste: Self-Supervised Learning for Anomaly Detection and Localization. In 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, Piscataway, NJ, 9659–9669.Google Scholar
- Tsung-Yi Lin, Priya Goyal, Ross Girshick, Kaiming He, and Piotr Dollár. 2017. Focal loss for dense object detection. In Proceedings of the IEEE international conference on computer vision. IEEE, Piscataway, NJ, 2980–2988.Google ScholarCross Ref
- Wenqian Liu, Runze Li, Meng Zheng, Srikrishna Karanam, Ziyan Wu, Bir Bhanu, Richard J Radke, and Octavia Camps. 2020. Towards visually explaining variational autoencoders. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. IEEE, Piscataway, NJ, 8642–8651.Google ScholarCross Ref
- Hyunjong Park, Jongyoun Noh, and Bumsub Ham. 2020. Learning memory-guided normality for anomaly detection. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. IEEE, Piscataway, NJ, 14372–14381.Google ScholarCross Ref
- Jonathan Pirnay and Keng Chai. 2022. Inpainting transformer for anomaly detection. In International Conference on Image Analysis and Processing. Springer, Cham, 394–406.Google ScholarDigital Library
- Olaf Ronneberger, Philipp Fischer, and Thomas Brox. 2015. U-net: Convolutional networks for biomedical image segmentation. In International Conference on Medical image computing and computer-assisted intervention. Springer, Cham, 234–241.Google ScholarCross Ref
- Mohammad Sabokrou, Mohammad Khalooei, Mahmood Fathy, and Ehsan Adeli. 2018. Adversarially Learned One-Class Classifier for Novelty Detection. In 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. IEEE, Piscataway, NJ, 3379–3388.Google Scholar
- Natasa Sarafijanovic-Djukic and Jesse Davis. 2019. Fast Distance-Based Anomaly Detection in Images Using an Inception-Like Autoencoder. In International Conference on Discovery Science. Springer, Cham, 493–508.Google ScholarDigital Library
- Thomas Schlegl, Philipp Seeböck, Sebastian M Waldstein, Ursula Schmidt-Erfurth, and Georg Langs. 2017. Unsupervised anomaly detection with generative adversarial networks to guide marker discovery. In International conference on information processing in medical imaging. Springer, Cham, 146–157.Google ScholarCross Ref
- Bernhard Schölkopf, John C Platt, John Shawe-Taylor, Alex J Smola, and Robert C Williamson. 2001. Estimating the support of a high-dimensional distribution. Neural computation 13, 7 (2001), 1443–1471.Google ScholarDigital Library
- Ramprasaath R Selvaraju, Michael Cogswell, Abhishek Das, Ramakrishna Vedantam, Devi Parikh, and Dhruv Batra. 2017. Grad-cam: Visual explanations from deep networks via gradient-based localization. In Proceedings of the IEEE international conference on computer vision. IEEE, Piscataway, NJ, 618–626.Google ScholarCross Ref
- Yong Shi, Jie Yang, and Zhiquan Qi. 2021. Unsupervised anomaly segmentation via deep feature reconstruction. Neurocomputing 424(2021), 9–22.Google ScholarCross Ref
- David MJ Tax and Robert PW Duin. 2004. Support vector data description. Machine learning 54, 1 (2004), 45–66.Google Scholar
- Guodong Wang, Shumin Han, Errui Ding, and Di Huang. 2021. Student-teacher feature pyramid matching for unsupervised anomaly detection. arXiv preprint arXiv:2103.04257(2021).Google Scholar
- Jihun Yi and Sungroh Yoon. 2020. Patch svdd: Patch-level svdd for anomaly detection and segmentation. In Proceedings of the Asian Conference on Computer Vision, Vol. 12627. Springer, Cham, 375–390.Google Scholar
- Muhammad Zaigham Zaheer, Jin-ha Lee, Marcella Astrid, and Seung-Ik Lee. 2020. Old is gold: Redefining the adversarially learned one-class classifier training paradigm. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. IEEE, Piscataway, NJ, 14183–14193.Google Scholar
- Vitjan Zavrtanik, Matej Kristan, and Danijel Skočaj. 2021a. Reconstruction by inpainting for visual anomaly detection. Pattern Recognition 112(2021), 107706.Google ScholarCross Ref
- Vitjan Zavrtanik, Matej Kristan, and Danijel Skočaj. 2021b. DRÆM – A discriminatively trained reconstruction embedding for surface anomaly detection. In 2021 IEEE/CVF International Conference on Computer Vision (ICCV). IEEE, Piscataway, NJ, 8310–8319.Google ScholarCross Ref
- Chong Zhou and Randy C Paffenroth. 2017. Anomaly detection with robust deep autoencoders. In Proceedings of the 23rd ACM SIGKDD international conference on knowledge discovery and data mining. ACM, New York, NY, 665–674.Google ScholarDigital Library
Index Terms
- Robust Anomaly Detection and Localization via Simulated Anomalies
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