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One-class classification (OCC) aims to learn an effective data description to enclose all normal training samples and detect anomalies based on the deviation from the data description.
Photoshopping Colonoscopy Video Frames
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Unsupervised learning of the set of local maxima
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Iterative energy-based projection on a normal data manifold for anomaly localization
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Multiscale structural similarity for image quality assessment
Wang, Z.; Simoncelli, E. P.; and Bovik, A. C. 2003 · 2003
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Deep-cascade: Cascading 3d deep neural networks for fast anomaly detection and localization in crowded scenes
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Image quality assessment: from error visibility to structural similarity
Wang, Z.; Bovik, A. C.; Sheikh, H. R.; and Simoncelli, E. P. 2004 · 2004
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Classification-based anomaly detection for general data
Bergman, L.; and Hoshen, Y. 2020 · 2005
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Pattern recognition and machine learning
Bishop, C. M. 2006 · 2006
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Knowledge Distillation: A Survey
Gou, J.; Yu, B.; Maybank, S. J.; and Tao, D. 2020 · 2006
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Dimensionality reduction by learning an invariant mapping
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Backpropagated Gradient Representations for Anomaly Detection
Kwon, G.; Prabhushankar, M.; Temel, D.; and AlRegib, G. 2020 · 2007
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Csi: Novelty detection via contrastive learning on distributionally shifted instances
Tack, J.; Mo, S.; Jeong, J.; and Shin, J. 2020 · 2007
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MNIST handwritten digit database
LeCun, Y.; Cortes, C.; and Burges, C. 2010 · 2010
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Stacked convolutional auto-encoders for hierarchical feature extraction
Masci, J.; and et al. 2011 · 2011
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Learning and evaluating representations for deep one-class classification
Sohn, K.; Li, C.-L.; Yoon, J.; Jin, M.; and Pfister, T. 2020 · 2011
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Auto-encoding variational bayes
Kingma, D. P.; and Welling, M. 2013 · 2013
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Generative Adversarial Nets
Goodfellow, I.; et al. 2014 · 2014
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The cifar-10 dataset
Krizhevsky, A.; Nair, V.; and Hinton, G. 2014 · 2014
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Striving for simplicity: The all convolutional net
Springenberg, J. T.; Dosovitskiy, A.; Brox, T.; and Riedmiller, M. 2014 · 2014
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Anomaly localization in topic-based analysis of surveillance videos
Pathak, D.; Sharang, A.; and Mukerjee, A. 2015 · 2015
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U-net: Convolutional networks for biomedical image segmentation
Ronneberger, O.; et al. 2015 · 2015
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Deep residual learning for image recognition
He, K.; Zhang, X.; Ren, S.; and Sun, J. 2016 · 2016
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Conditional image generation with pixelcnn decoders
Van den Oord, A.; et al. 2016 · 2016
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What’s wrong with that object? Identifying images of unusual objects by modelling the detection score distribution
Wang, P.; Liu, L.; Shen, C.; Huang, Z.; van den Hengel, A.; and Shen, H. T. 2016 · 2016
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Unsupervised monocular depth estimation with left-right consistency
Godard, C.; Mac Aodha, O.; and Brostow, G. J. 2017 · 2017
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Unsupervised anomaly detection with generative adversarial networks to guide marker discovery
Schlegl, T.; Seeböck, P.; Waldstein, S. M.; Schmidt-Erfurth, U.; and Langs, G. 2017 · 2017
Ocgan: One-class novelty detection using gans with constrained latent representations
Perera, P.; Nallapati, R.; and Xiang, B. 2019 · 2019
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Learning deep features for one-class classification
Perera, P.; and Patel, V. M. 2019 · 2019
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Ocgan: One-class novelty detection using gans with constrained latent representations
Perera, P.; et al. 2019 · 2019
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f-AnoGAN: Fast unsupervised anomaly detection with generative adversarial networks
Schlegl, T.; Seeböck, P.; Waldstein, S. M.; Langs, G.; and Schmidt-Erfurth, U. 2019 · 2019
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Learning deep features for discriminative localization
Zhou, B.; et al. 2016 · 2019
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Uninformed students: Student-teacher anomaly detection with discriminative latent embeddings
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Smoothgrad: removing noise by adding noise
Smilkov, D.; Thorat, N.; Kim, B.; Viégas, F.; and Wattenberg, M. 2017 · 2017
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Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms
Xiao, H.; Rasul, K.; and Vollgraf, R. 2017 · 2017
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mixup: Beyond empirical risk minimization
Zhang, H.; et al. 2017 · 2017
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Ganomaly: Semi-supervised anomaly detection via adversarial training
Akcay, S.; et al. 2018 · 2018
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Deep autoencoding models for unsupervised anomaly segmentation in brain MR images
Baur, C.; Wiestler, B.; Albarqouni, S.; and Navab, N. 2018 · 2018
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Improving unsupervised defect segmentation by applying structural similarity to autoencoders
Bergmann, P.; Löwe, S.; Fauser, M.; Sattlegger, D.; and Steger, C. 2018 · 2018
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Bergmann, P.; et al. 2020 · 2020
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HyperKvasir, a comprehensive multi-class image and video dataset for gastrointestinal endoscopy
Borgli, H.; and et al. 2020 · 2020
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A simple framework for contrastive learning of visual representations
Chen, T.; et al. 2020 · 2020
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Exploring deep anomaly detection methods based on capsule net
Li, X.; et al. 2020 · 2020
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Towards visually explaining variational autoencoders
Liu, W.; et al. 2020 · 2020
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Tutorial on EM algorithm
Nguyen, L. 2020 · 2020
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Deep semi-supervised anomaly detection
Ruff, L.; et al. 2020 · 2020
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Anomaly Detection Neural Network with Dual Auto-Encoders GAN and Its Industrial Inspection Applications
Tang, T.-W.; et al. 2020 · 2020
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Few-Shot Anomaly Detection for Polyp Frames from Colonoscopy
Tian, Y.; Maicas, G.; Pu, L. Z. C. T.; Singh, R.; Verjans, J. W.; and Carneiro, G. 2020 · 2020
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Padim: a patch distribution modeling framework for anomaly detection and localization
Defard, T.; Setkov, A.; Loesch, A.; and Audigier, R. 2021 · 2021
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CutPaste: Self-Supervised Learning for Anomaly Detection and Localization
Li, C.-L.; et al. 2021 · 2021
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Deep learning for anomaly detection: A review
Pang, G.; Shen, C.; Cao, L.; and Hengel, A. V. D. 2021 · 2021
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PANDA: Adapting Pretrained Features for Anomaly Detection and Segmentation
Reiss, T.; Cohen, N.; Bergman, L.; and Hoshen, Y. 2021 · 2021
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Mean-Shifted Contrastive Loss for Anomaly Detection
Reiss, T.; and Hoshen, Y. 2021 · 2021
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Multiresolution Knowledge Distillation for Anomaly Detection
Salehi, M.; et al. 2021 · 2021
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Glancing at the Patch: Anomaly Localization With Global and Local Feature Comparison
Wang, S.; et al. 2021 · 2021
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Learning Semantic Context from Normal Samples for Unsupervised Anomaly Detection
Yan, X.; et al. 2021 · 2021
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DRAEM - A Discriminatively Trained Reconstruction Embedding for Surface Anomaly Detection
Zavrtanik, V.; et al. 2021 · 2021
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