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We consider the problem of anomaly detection in images, and present a new detection technique.
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Robust principal component analysis?
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A survey on unsupervised outlier detection in high-dimensional numerical data
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Generative adversarial nets
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Wide residual networks
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Deep structured energy based models for anomaly detection
S. Zhai, Y. Cheng, W. Lu, and Z. Zhang · 2016
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Deep active learning over the long tail
Y. Geifman and R. El-Yaniv · 2017
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Y. Geifman and R. El-Yaniv · 2017
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Unsupervised anomaly detection with generative adversarial networks to guide marker discovery
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Fashion-mnist: a novel image dataset for benchmarking machine learning algorithms, 2017
H. Xiao, K. Rasul, and R. Vollgraf · 2017
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Variational autoencoder based anomaly detection using reconstruction probability
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Model selection for anomaly detection
E. Burnaev, P. Erofeev, and D. Smolyakov · 2015
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Unsupervised representation learning with deep convolutional generative adversarial networks
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Learning discriminative reconstructions for unsupervised outlier removal
Y. Xia, X. Cao, F. Wen, G. Hua, and J. Sun · 2015
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Understanding neural networks through deep visualization
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Multi-view anomaly detection via robust probabilistic latent variable models
T. Iwata and M. Yamada · 2016
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Anomaly detection with generative adversarial networks, 2018
L. Deecke, R. Vandermeulen, L. Ruff, S. Mandt, and M. Kloft · 2018
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Boosting uncertainty estimation for deep neural classifiers
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Deep one-class classification
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Hyperparameter selection of one-class support vector machine by self-adaptive data shifting
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Deep autoencoding gaussian mixture model for unsupervised anomaly detection
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