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Convolutional autoencoders have emerged as popular methods for unsupervised defect segmentation on image data.
Multiscale structural similarity for image quality assessment
Zhou Wang, Eero P Simoncelli, and Alan C Bovik · 2003
Earlier work this paper cites.
Image quality assessment: from error visibility to structural similarity
Zhou Wang, Alan C Bovik, Hamid R Sheikh, and Eero P Simoncelli · 2004
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ImageNet Classification With Deep Convolutional Neural Networks
Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton · 2012
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Novelty Detection in Images by Sparse Representations
Giacomo Boracchi, Diego Carrera, and Brendt Wohlberg · 2014
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Generative Adversarial Nets
Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio · 2014
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Auto-Encoding Variational Bayes
Diederik P Kingma and Max Welling · 2014
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A review of novelty detection
Marco AF Pimentel, David A Clifton, Lei Clifton, and Lionel Tarassenko · 2014
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Variational Autoencoder based Anomaly Detection using Reconstruction Probability
Jinwon An and Sungzoon Cho · 2015
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Detecting anomalous structures by convolutional sparse models
Diego Carrera, Giacomo Boracchi, Alessandro Foi, and Brendt Wohlberg · 2015
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Adam: A Method for Stochastic Optimization
Diederik P Kingma and Jimmy Ba · 2015
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Learning to generate images with perceptual similarity metrics
Karl Ridgeway, Jake Snell, Brett Roads, Richard S Zemel, and Michael C Mozer · 2015
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ImageNet Large Scale Visual Recognition Challenge
Olga Russakovsky, Jia Deng, Hao Su, Jonathan Krause, Sanjeev Satheesh, Sean Ma, Zhiheng Huang, Andrej Karpathy, Aditya Khosla, Michael Bernstein, et al · 2015
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Scale-invariant anomaly detection with multiscale group-sparse models
Diego Carrera, Giacomo Boracchi, Alessandro Foi, and Brendt Wohlberg · 2016
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Generating Images with Perceptual Similarity Metrics based on Deep Networks
Alexey Dosovitskiy and Thomas Brox · 2016
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Deep Learning
Unsupervised Anomaly Detection with Generative Adversarial Networks to Guide Marker Discovery
Thomas Schlegl, Philipp Seeböck, Sebastian M Waldstein, Ursula Schmidt-Erfurth, and Georg Langs · 2017
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Deep Autoencoding Models for Unsupervised Anomaly Segmentation in Brain MR Images
Christoph Baur, Benedikt Wiestler, Shadi Albarqouni, and Nassir Navab · 2018
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Anomaly Detection in Nanofibrous Materials by CNN-Based Self-Similarity
Paolo Napoletano, Flavio Piccoli, and Raimondo Schettini · 2018
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Learning Deep Features for One-Class Classification
Pramuditha Perera and Vishal M Patel · 2018
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Adversarially Learned One-Class Classifier for Novelty Detection
Mohammad Sabokrou, Mohammad Khalooei, Mahmood Fathy, and Ehsan Adeli · 2018
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Ian Goodfellow, Yoshua Bengio, and Aaron Courville · 2016
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Towards Principled Methods for Training Generative Adversarial Networks
Martin Arjovsky and Léon Bottou · 2017
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Defect Detection in SEM Images of Nanofibrous Materials
Diego Carrera, Fabio Manganini, Giacomo Boracchi, and Ettore Lanzarone · 2017
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Adversarial Feature Learning
Jeff Donahue, Philipp Krähenbühl, and Trevor Darrell · 2017
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Reliably Decoding Autoencoders’ Latent Spaces for One-Class Learning Image Inspection Scenarios
Daniel Soukup and Thomas Pinetz · 2018
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Machine Vision Algorithms and Applications
Carsten Steger, Markus Ulrich, and Christian Wiedemann · 2018
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q-Space Novelty Detection with Variational Autoencoders
Aleksei Vasilev, Vladimir Golkov, Ilona Lipp, Eleonora Sgarlata, Valentina Tomassini, Derek K Jones, and Daniel Cremers · 2018
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Efficient GAN-Based Anomaly Detection
Houssam Zenati, Chuan Sheng Foo, Bruno Lecouat, Gaurav Manek, and Vijay Ramaseshan Chandrasekhar · 2018
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