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Autoencoders (AE) have recently been widely employed to approach the novelty detection problem.
Deep learning for anomaly detection: A survey
Chalapathy, R.; and Chawla, S. 2019 · 1901
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Spatio-temporal autoencoder for video anomaly detection
Zhao, Y.; Deng, B.; Shen, C.; Liu, Y.; Lu, H.; and Hua, X.-S. 2017 · 1941
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Statistical decision functions which minimize the maximum risk
Wald, A. 1945 · 1945
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Identification of outliers , volume 11
Hawkins, D. M. 1980 · 1980
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object image library (COIL-100
Nene, S. A.; Nayar, S. K.; and Murase, H. 1996 · 1996
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LOF: identifying density-based local outliers
Breunig, M. M.; Kriegel, H.-P.; Ng, R. T.; and Sander, J. 2000 · 2000
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One-class SVM for learning in image retrieval
Chen, Y.; Zhou, X. S.; and Huang, T. S. 2001 · 2001
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Learning Multiple Layers of Features from Tiny Images
Krizhevsky, A. 2009 · 2009
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MNIST handwritten digit database
LeCun, Y.; Cortes, C.; and Burges, C. 2010 · 2010
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Robust PCA via outlier pursuit
Xu, H.; Caramanis, C.; and Sanghavi, S. 2010 · 2010
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Sensitivity, Specificity, Accuracy, Associated Confidence Interval and ROC Analysis with Practical SAS Implementations
Zhu, W.; Zeng, N.; and Wang, N. 2010 · 2010
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A geometric analysis of subspace clustering with outliers
Soltanolkotabi, M.; Candes, E. J.; et al. 2012 · 2012
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Auto-encoding variational bayes
Kingma, D. P.; and Welling, M. 2013 · 2013
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Visualizing and understanding convolutional networks
Zeiler, M. D.; and Fergus, R. 2014 · 2014
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Autoencoding beyond pixels using a learned similarity metric
Larsen, A. B. L.; Sønderby, S. K.; Larochelle, H.; and Winther, O. 2015 · 2015
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Learning discriminative reconstructions for unsupervised outlier removal
Xia, Y.; Cao, X.; Wen, F.; Hua, G.; and Sun, J. 2015 · 2015
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NIPS 2016 tutorial: Generative adversarial networks
Goodfellow, I. 2016 · 2016
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Learning temporal regularity in video sequences
Hasan, M.; Choi, J.; Neumann, J.; Roy-Chowdhury, A. K.; and Davis, L. S. 2016 · 2016
Cited alongside, same era.
Image restoration using convolutional auto-encoders with symmetric skip connections
Mao, X.-J.; Shen, C.; and Yang, Y.-B. 2016 · 2016
Cited alongside, same era.
Towards principled methods for training generative adversarial networks
Martin, A.; and Lon, B. 2017 · 2016
Cited alongside, same era.
Improved techniques for training gans
Salimans, T.; Goodfellow, I.; Zaremba, W.; Cheung, V.; Radford, A.; and Chen, X. 2016 · 2016
Cited alongside, same era.
Generative probabilistic novelty detection with adversarial autoencoders
Pidhorskyi, S.; Almohsen, R.; and Doretto, G. 2018 · 2018
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Deep one-class classification
Ruff, L.; Vandermeulen, R.; Goernitz, N.; Deecke, L.; Siddiqui, S. A.; Binder, A.; Müller, E.; and Kloft, M. 2018 · 2018
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Adversarially learned one-class classifier for novelty detection
Sabokrou, M.; Khalooei, M.; Fathy, M.; and Adeli, E. 2018 · 2018
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Real-world anomaly detection in surveillance videos
Sultani, W.; Chen, C.; and Shah, M. 2018 · 2018
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Dual principal component pursuit
Tsakiris, M. C.; and Vidal, R. 2018 · 2018
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Robustness may be at odds with accuracy
Tsipras, D.; Santurkar, S.; Engstrom, L.; Turner, A.; and Madry, A. 2018 · 2018
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Zhai, S.; Cheng, Y.; Lu, W.; and Zhang, Z. 2016 · 2016
Cited alongside, same era.
Towards better understanding of gradient-based attribution methods for deep neural networks
Ancona, M.; Ceolini, E.; Öztireli, C.; and Gross, M. 2017 · 2017
Cited alongside, same era.
Exploring the landscape of spatial robustness
Engstrom, L.; Tran, B.; Tsipras, D.; Schmidt, L.; and Madry, A. 2017 · 2017
Cited alongside, same era.
On convergence and stability of gans
Kodali, N.; Abernethy, J.; Hays, J.; and Kira, Z. 2017 · 2017
Cited alongside, same era.
Towards deep learning models resistant to adversarial attacks
Madry, A.; Makelov, A.; Schmidt, L.; Tsipras, D.; and Vladu, A. 2017 · 2017
Cited alongside, same era.
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
Cited alongside, same era.
Fashion-mnist: a novel image dataset for benchmarking machine learning algorithms
Xiao, H.; Rasul, K.; and Vollgraf, R. 2017 · 2017
Cited alongside, same era.
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Deep autoencoding gaussian mixture model for unsupervised anomaly detection
Zong, B.; Song, Q.; Min, M. R.; Cheng, W.; Lumezanu, C.; Cho, D.; and Chen, H. 2018 · 2018
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Latent space autoregression for novelty detection
Abati, D.; Porrello, A.; Calderara, S.; and Cucchiara, R. 2019 · 2019
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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. 2019 · 2019
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Brain MRI Images for Brain Tumor Detection
Chakrabarty, N. 2019 · 2019
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Memorizing normality to detect anomaly: Memory-augmented deep autoencoder for unsupervised anomaly detection
Gong, D.; Liu, L.; Le, V.; Saha, B.; Mansour, M. R.; Venkatesh, S.; and Hengel, A. v. d. 2019 · 2019
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Adversarial examples are not bugs, they are features
Ilyas, A.; Santurkar, S.; Tsipras, D.; Engstrom, L.; Tran, B.; and Madry, A. 2019 · 2019
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Ocgan: One-class novelty detection using gans with constrained latent representations
Perera, P.; Nallapati, R.; and Xiang, B. 2019 · 2019
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Adversarial training and robustness for multiple perturbations
Tramèr, F.; and Boneh, D. 2019 · 2019
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Multivariate Triangular Quantile Maps for Novelty Detection
Wang, J.; Sun, S.; and Yu, Y. 2019 · 2019
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