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Deep autoencoder has been extensively used for anomaly detection.
On estimation of a probability density function and mode
E. Parzen · 1962
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The mnist database of handwritten digits
Y. LeCun · 1998
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Support vector method for novelty detection
B. Schölkopf, R. C. Williamson, A. J. Smola, J. Shawe-Taylor, and J. C. Platt · 2000
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One-class svm for learning in image retrieval
Y. Chen, X. S. Zhou, and T. S. Huang · 2001
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Learning with kernels: support vector machines, regularization, optimization, and beyond
B. Scholkopf and A. J. Smola · 2001
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Greedy layer-wise training of deep networks
Y. Bengio, P. Lamblin, D. Popovici, and H. Larochelle · 2007
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Anomaly detection: A survey
V. Chandola, A. Banerjee, and V. Kumar · 2009
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Observe locally, infer globally: a space-time mrf for detecting abnormal activities with incremental updates
J. Kim and K. Grauman · 2009
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Learning multiple layers of features from tiny images
A. Krizhevsky and G. Hinton · 2009
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Rectified linear units improve restricted boltzmann machines
V. Nair and G. E. Hinton · 2010
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Principal component analysis
I. Jolliffe · 2011
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Group anomaly detection using flexible genre models
L. Xiong, B. Póczos, and J. G. Schneider · 2011
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Online detection of unusual events in videos via dynamic sparse coding
B. Zhao, L. Fei-Fei, and E. P. Xing · 2011
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A survey on unsupervised outlier detection in high-dimensional numerical data
A. Zimek, E. Schubert, and H.-P. Kriegel · 2012
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Auto-encoding variational bayes
D. P. Kingma and M. Welling · 2013
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Uci machine learning repository, 2013
M. Lichman et al · 2013
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Abnormal event detection at 150 fps in matlab
C. Lu, J. Shi, and J. Jia · 2013
Cited alongside, same era.
A. Graves, G. Wayne, and I. Danihelka · 2014
Cited alongside, same era.
Adam: A method for stochastic optimization
D. P. Kingma and J. Ba · 2014
Cited alongside, same era.
Batch normalization: Accelerating deep network training by reducing internal covariate shift
S. Ioffe and C. Szegedy · 2015
Cited alongside, same era.
Learning spatiotemporal features with 3d convolutional networks
D. Tran, L. Bourdev, R. Fergus, L. Torresani, and M. Paluri · 2015
Cited alongside, same era.
The lv dataset: A realistic surveillance video dataset for abnormal event detection
R. Leyva, V. Sanchez, and C.-T. Li · 2017
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A revisit of sparse coding based anomaly detection in stacked rnn framework
W. Luo, W. Liu, and S. Gao · 2017
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Automatic differentiation in pytorch
A. Paszke, S. Gross, S. Chintala, G. Chanan, E. Yang, Z. DeVito, Z. Lin, A. Desmaison, L. Antiga, and A. Lerer · 2017
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Unmasking the abnormal events in video
R. Tudor Ionescu, S. Smeureanu, B. Alexe, and M. Popescu · 2017
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Joint learning of unsupervised dimensionality reduction and gaussian mixture model
X. Yang, K. Huang, J. Y. Goulermas, and R. Zhang · 2017
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Spatio-temporal autoencoder for video anomaly detection
Y. Zhao, B. Deng, C. Shen, Y. Liu, H. Lu, and X.-S. Hua · 2017
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J. Weston, S. Chopra, and A. Bordes · 2015
Cited alongside, same era.
Learning deep representations of appearance and motion for anomalous event detection
D. Xu, E. Ricci, Y. Yan, J. Song, and N. Sebe · 2015
Cited alongside, same era.
Learning temporal regularity in video sequences
M. Hasan, J. Choi, J. Neumann, A. K. Roy-Chowdhury, and L. S. Davis · 2016
Cited alongside, same era.
Learning to generate with memory
C. Li, J. Zhu, and B. Zhang · 2016
Cited alongside, same era.
Scaling memory-augmented neural networks with sparse reads and writes
J. Rae, J. J. Hunt, I. Danihelka, T. Harley, A. W. Senior, G. Wayne, A. Graves, and T. Lillicrap · 2016
Cited alongside, same era.
One-shot learning with memory-augmented neural networks
A. Santoro, S. Bartunov, M. Botvinick, D. Wierstra, and T. Lillicrap · 2016
Cited alongside, same era.
Conditional image generation with pixelcnn decoders
A. van den Oord, N. Kalchbrenner, L. Espeholt, O. Vinyals, A. Graves, et al · 2016
Cited alongside, same era.
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Anomaly detection with robust deep autoencoders
C. Zhou and R. C. Paffenroth · 2017
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AND: Autoregressive novelty detectors
D. Abati, A. Porrello, S. Calderara, and R. Cucchiara · 2018
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Anomaly detection using one-class neural networks
R. Chalapathy, A. K. Menon, and S. Chawla · 2018
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Deep anomaly detection using geometric transformations
I. Golan and R. El-Yaniv · 2018
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Memorization precedes generation: Learning unsupervised gans with memory networks
Y. Kim, M. Kim, and G. Kim · 2018
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Future frame prediction for anomaly detection–a new baseline
W. Liu, W. Luo, D. Lian, and S. Gao · 2018
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Deep one-class classification
L. Ruff, R. Vandermeulen, N. Goernitz, L. Deecke, S. A. Siddiqui, A. Binder, E. Müller, and M. Kloft · 2018
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Adversarially learned one-class classifier for novelty detection
M. Sabokrou, M. Khalooei, M. Fathy, and E. Adeli · 2018
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Deep autoencoding gaussian mixture model for unsupervised anomaly detection
B. Zong, Q. Song, M. R. Min, W. Cheng, C. Lumezanu, D. Cho, and H. Chen · 2018
Later among the works it cites.