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3D anomaly detection is an emerging and vital computer vision task in industrial manufacturing (IM).
Same Same But DifferNet: Semi-Supervised Defect Detection with Normalizing Flows
Marco Rudolph, Bastian Wandt, and Bodo Rosenhahn. 2020 · 1915
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Same same but differnet: Semi-supervised defect detection with normalizing flows
Marco Rudolph, Bastian Wandt, and Bodo Rosenhahn. 2021 · 1916
Earlier work this paper cites.
Image quality assessment: from error visibility to structural similarity
Zhou Wang, Alan Conrad Bovik, Hamid R. Sheikh, and Eero P. Simoncelli. 2004 · 2004
Earlier work this paper cites.
Focal Loss for Dense Object Detection
Tsung-Yi Lin, Priya Goyal, Ross B. Girshick, Kaiming He, and Piotr Dollár. 2017 · 2017
Earlier work this paper cites.
GANomaly: Semi-Supervised Anomaly Detection via Adversarial Training. In Asian Conference on Computer Vision
Samet Akçay, Amir Atapour-Abarghouei, and T. Breckon. 2018 · 2018
Earlier work this paper cites.
Probabilistic Modeling of Deep Features for Out-of-Distribution and Adversarial Detection
Nilesh A. Ahuja, Ibrahima J. Ndiour, Trushant Kalyanpur, and Omesh Tickoo. 2019 · 2019
Earlier work this paper cites.
MVTec AD–A comprehensive real-world dataset for unsupervised anomaly detection
Paul Bergmann, Michael Fauser, David Sattlegger, and Carsten Steger. 2019 · 2019
Earlier work this paper cites.
Uninformed students: Student-teacher anomaly detection with discriminative latent embeddings
Paul Bergmann, Michael Fauser, David Sattlegger, and Carsten Steger. 2020 · 2020
Earlier work this paper cites.
Sub-Image Anomaly Detection with Deep Pyramid Correspondences
Niv Cohen and Yedid Hoshen. 2020 · 2020
Earlier work this paper cites.
PaDiM: a Patch Distribution Modeling Framework for Anomaly Detection and Localization. In ICPR Workshops
Thomas Defard, Aleksandr Setkov, Angelique Loesch, and Romaric Audigier. 2020 · 2020
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The MVTec 3D-AD Dataset for Unsupervised 3D Anomaly Detection and Localization
Paul Bergmann, Xin Jin, David Sattlegger, and Carsten Steger. 2021 · 2021
Earlier work this paper cites.
Padim: a patch distribution modeling framework for anomaly detection and localization
Thomas Defard, Aleksandr Setkov, Angelique Loesch, and Romaric Audigier. 2021 · 2021
Earlier work this paper cites.
CFLOW-AD: Real-Time Unsupervised Anomaly Detection with Localization via Conditional Normalizing Flows
Denis A. Gudovskiy, Shun Ishizaka, and Kazuki Kozuka. 2021 · 2021
Earlier work this paper cites.
Cutpaste: Self-supervised learning for anomaly detection and localization
Chun-Liang Li, Kihyuk Sohn, Jinsung Yoon, and Tomas Pfister. 2021 · 2021
Cited alongside, same era.
Towards Total Recall in Industrial Anomaly Detection
Karsten Roth, Latha Pemula, Joaquin Zepeda, Bernhard Schölkopf, Thomas Brox, and Peter Gehler. 2021 · 2021
Cited alongside, same era.
Student-Teacher Feature Pyramid Matching for Anomaly Detection. In British Machine Vision Conference
Guodong Wang, Shumin Han, Errui Ding, and Di Huang. 2021 · 2021
Cited alongside, same era.
FastFlow: Unsupervised Anomaly Detection and Localization via 2D Normalizing Flows
Jiawei Yu1, Ye Zheng, Xiang Wang, Wei Li, Yushuang Wu, Rui Zhao, and Liwei Wu. 2021 · 2021
Cited alongside, same era.
Draem-a discriminatively trained reconstruction embedding for surface anomaly detection
Vitjan Zavrtanik, Matej Kristan, and Danijel Skočaj. 2021a · 2021
Cited alongside, same era.
Towards Continual Adaptation in Industrial Anomaly Detection
Wujin Li, Jiawei Zhan, Jinbao Wang, Bizhong Xia, Bin-Bin Gao, Jun Liu, Chengjie Wang, and Feng Zheng. 2022 · 2022
Later among the works it cites.
Towards total recall in industrial anomaly detection
Karsten Roth, Latha Pemula, Joaquin Zepeda, Bernhard Schölkopf, Thomas Brox, and Peter Gehler. 2022 · 2022
Later among the works it cites.
Asymmetric Student-Teacher Networks for Industrial Anomaly Detection
Marco Rudolph, Tom Wehrbein, Bodo Rosenhahn, and Bastian Wandt. 2022a · 2022
Later among the works it cites.
Asymmetric Student-Teacher Networks for Industrial Anomaly Detection
Marco Rudolph, Tom Wehrbein, Bodo Rosenhahn, and Bastian Wandt. 2022b · 2022
Later among the works it cites.
Natural Synthetic Anomalies for Self-supervised Anomaly Detection and Localization
Hannah M Schlüter, Jeremy Tan, Benjamin Hou, and Bernhard Kainz. 2022 · 2022
Later among the works it cites.
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DRAEM – A discriminatively trained reconstruction embedding for surface anomaly detection
Vitjan Zavrtanik, Matej Kristan, and Danijel Skočaj. 2021b · 2021
Cited alongside, same era.
Anomalib: A Deep Learning Library for Anomaly Detection
Samet Akcay, Dick Ameln, Ashwin Vaidya, Barath Lakshmanan, Nilesh Ahuja, and Utku Genc. 2022 · 2022
Cited alongside, same era.
Anomaly Detection in 3D Point Clouds using Deep Geometric Descriptors
Paul Bergmann and David Sattlegger. 2022 · 2022
Cited alongside, same era.
The Eyecandies Dataset for Unsupervised Multimodal Anomaly Detection and Localization
Luca Bonfiglioli, Marco Toschi, Davide Silvestri, Nicola Fioraio, and Daniele De Gregorio. 2022a · 2022
Cited alongside, same era.
Anomaly Detection via Reverse Distillation from One-Class Embedding
Hanqiu Deng and Xingyu Li. 2022 · 2022
Cited alongside, same era.
Cflow-ad: Real-time unsupervised anomaly detection with localization via conditional normalizing flows
Denis Gudovskiy, Shun Ishizaka, and Kazuki Kozuka. 2022 · 2022
Cited alongside, same era.
Back to the feature: classical 3d features are (almost) all you need for 3d anomaly detection
Eliahu Horwitz and Yedid Hoshen. 2022a · 2022
Cited alongside, same era.
Zhiyuan You, Lei Cui, Yujun Shen, Kai Yang, Xin Lu, Yu Zheng, and Xinyi Le. 2022 · 2022
Later among the works it cites.
DSR–A dual subspace re-projection network for surface anomaly detection
Vitjan Zavrtanik, Matej Kristan, and Danijel Skočaj. 2022 · 2022
Later among the works it cites.
Complementary Pseudo Multimodal Feature for Point Cloud Anomaly Detection
Yunkang Cao, Xiaohao Xu, and Weiming Shen. 2023 · 2023
Closest in time.
Squeeze-and-Excitation Networks
Jie Hu, Li Shen, Samuel Albanie, Gang Sun, and Enhua Wu. 2017 · 2023
Closest in time.
SimpleNet: A Simple Network for Image Anomaly Detection and Localization
Zhikang Liu, Yiming Zhou, Yuansheng Xu, and Zilei Wang. 2023b · 2023
Closest in time.
Multimodal Industrial Anomaly Detection via Hybrid Fusion
Yue Wang, Jinlong Peng, Jiangning Zhang, Ran Yi, Yabiao Wang, and Chengjie Wang. 2023 · 2023
Closest in time.
IM-IAD: Industrial Image Anomaly Detection Benchmark in Manufacturing
Guoyang Xie, Jinbao Wang, Jiaqi Liu, Jiayi Lyu, Y. Liu, Chengjie Wang, Feng Zheng, and Yaochu Jin. 2023b · 2023
Closest in time.