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This paper introduces anomalib, a novel library for unsupervised anomaly detection and localization.
“Learning multiple layers of features from tiny images,”
Alex Krizhevsky, · 2009
Earlier work this paper cites.
“Scikit-learn: Machine Learning in Python,”
Fabian Pedregosa, Vincent Michel, Olivier Grisel, Mathieu Blondel, Peter Prettenhofer, Ron Weiss, Jake Vanderplas, David Cournapeau, Fabian Pedregosa, Gaël Varoquaux, Alexandre Gramfort, Bertrand Thirion, Olivier Grisel, Vincent Dubourg, Alexandre Passos, and Matthieu Brucher, · 2011
Earlier work this paper cites.
“TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems,”
Martín Abadi, Ashish Agarwal, Paul Barham, Eugene Brevdo, Zhifeng Chen, Craig Citro, Greg S Corrado, Andy Davis, Jeffrey Dean, Matthieu Devin, Sanjay Ghemawat, Ian Goodfellow, Andrew Harp, Geoffrey Irving, Michael Isard, Yangqing Jia, Rafal Jozefowicz, Lukasz Kaiser, Manjunath Kudlur, Josh Levenberg, Dan Mané, Rajat Monga, Sherry Moore, Derek Murray, Chris Olah, Mike Schuster, Jonathon Shlens, Benoit Steiner, Ilya Sutskever, Kunal Talwar, Paul Tucker, Vincent Vanhoucke, Vijay Vasudevan, Fernanda Viégas, Oriol Vinyals, Pete Warden, Martin Wattenberg, Martin Wicke, Yuan Yu, and Xiaoqiang Zheng, · 2015
Earlier work this paper cites.
“A Revisit of Sparse Coding Based Anomaly Detection in Stacked RNN Framework,”
Weixin Luo, Wen Liu, and Shenghua Gao, · 2017
Earlier work this paper cites.
“Active Learning for Convolutional Neural Networks: A Core-Set Approach,”
Ozan Sener and Silvio Savarese, · 2018
Earlier work this paper cites.
“MMDetection: Open MMLab Detection Toolbox and Benchmark,”
Kai Chen, Jiaqi Wang, Jiangmiao Pang, Yuhang Cao, Yu Xiong, Xiaoxiao Li, Shuyang Sun, Wansen Feng, Ziwei Liu, Jiarui Xu, Zheng Zhang, Dazhi Cheng, Chenchen Zhu, Tianheng Cheng, Qijie Zhao, Buyu Li, Xin Lu, Rui Zhu, Yue Wu, Jifeng Dai, Jingdong Wang, Jianping Shi, Wanli Ouyang, Chen Change Loy, and Dahua Lin, · 2019
Earlier work this paper cites.
“Detectron2,” https://github.com/facebookresearch/detectron2, 2019
Yuxin Wu, Alexander Kirillov, Francisco Massa, Wan-Yen Lo, and Ross Girshick, · 2019
Earlier work this paper cites.
“PyOD: A Python Toolbox for Scalable Outlier Detection,”
Yue Zhao, Zain Nasrullah, and Zheng Li, · 2019
Earlier work this paper cites.
“PyTorch: An Imperative Style, High-Performance Deep Learning Library,”
Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, Alban Desmaison, Andreas Köpf, Edward Yang, Zach DeVito, Martin Raison, Alykhan Tejani, Sasank Chilamkurthy, Benoit Steiner, Lu Fang, Junjie Bai, and Soumith Chintala, · 2019
Cited alongside, same era.
“OpenVINO deep learning workbench: Comprehensive analysis and tuning of neural networks inference,”
Alexander Demidovskij, Yury Gorbachev, Mikhail Fedorov, Iliya Slavutin, Artyom Tugarev, Marat Fatekhov, and Yaroslav Tarkan, · 2019
Cited alongside, same era.
“MVTec AD — A Comprehensive Real-World Dataset for Unsupervised Anomaly Detection,”
Paul Bergmann, Michael Fauser, David Sattlegger, and Carsten Steger, · 2019
Cited alongside, same era.
“Probabilistic Modeling of Deep Features for Out-of-Distribution and Adversarial Detection,”
Nilesh A. Ahuja, Ibrahima Ndiour, Trushant Kalyanpur, and Omesh Tickoo, · 2019
Cited alongside, same era.
“GANomaly: Semi-supervised Anomaly Detection via Adversarial Training,”
Samet Akcay, Amir Atapour-Abarghouei, and Toby Breckon, · 2019
“PyTorch Lightning,” 5 2020
William Falcon, Jirka Borovec, Adrian Wälchli, Nic Eggert, Justus Schock, Jeremy Jordan, Nicki Skafte, Vadim Bereznyuk, Ethan Harris, Tullie Murrell, Peter Yu, Sebastian Præsius, Travis Addair, Jacob Zhong, Dmitry Lipin, So Uchida, Shreyas Bapat, Hendrik Schröter, Boris Dayma, Alexey Karnachev, Akshay Kulkarni, Shunta Komatsu, Hadrien Mary, Donal Byrne, Cristobal Eyzaguirre, and Anton Bakhtin, · 2020
Later among the works it cites.
“Experiment Tracking with Weights and Biases,” 2020
Lukas Biewald, · 2020
Later among the works it cites.
“VT-ADL: A Vision Transformer Network for Image Anomaly Detection and Localization,”
Pankaj Mishra, Riccardo Verk, Daniele Fornasier, Claudio Piciarelli, and Gian Luca Foresti, · 2021
Later among the works it cites.
“PaDiM: A Patch Distribution Modeling Framework for Anomaly Detection and Localization,”
Thomas Defard, Alexander Setkov, Angelique Loesch, and Romaric Audigier, · 2021
Later among the works it cites.
“CFLOW-AD: Real-Time Unsupervised Anomaly Detection with Localization via Conditional Normalizing Flows,”
Denis Gudovskiy, Shun Ishizaka, and Kazuki Kozuka, · 2021
Later among the works it cites.
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Cited alongside, same era.
“Segmentation-based deep-learning approach for surface-defect detection,”
Domen Tabernik, Samo Šela, Jure Skvarč, and Danijel Skočaj, · 2020
Cited alongside, same era.
“Albumentations: Fast and Flexible Image Augmentations,”
Alexander Buslaev, Vladimir I. Iglovikov, Eugene Khvedchenya, Alex Parinov, Mikhail Druzhinin, and Alexandr A. Kalinin, · 2020
Cited alongside, same era.
“Neural Network Compression Framework for fast model inference,”
Alexander Kozlov, Ivan Lazarevich, Vasily Shamporov, Nikolay Lyalyushkin, and Yury Gorbachev, · 2020
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
Later among the works it cites.
“Student-Teacher Feature Pyramid Matching for Unsupervised Anomaly Detection,”
Guodong Wang, Shumin Han, Errui Ding, and Di Huang, · 2021
Later among the works it cites.