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Novelty detection is commonly referred to as the discrimination of observations that do not conform to a learned model of regularity.
Thermostatics and thermodynamics: an introduction to energy, information and states of matter, with engineering applications
M. Tribus · 1961
Earlier work this paper cites.
Support vector method for novelty detection
B. Schölkopf, R. C. Williamson, A. J. Smola, J. Shawe-Taylor, and J. C. Platt · 2000
Earlier work this paper cites.
Robust real-time unusual event detection using multiple fixed-location monitors
A. Adam, E. Rivlin, I. Shimshoni, and D. Reinitz · 2008
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Learning object motion patterns for anomaly detection and improved object detection
A. Basharat, A. Gritai, and M. Shah · 2008
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Ucsd pedestrian database
A. Chan and N. Vasconcelos · 2008
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Computer-vision-based fabric defect detection: A survey
A. Kumar · 2008
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Bayesian surprise attracts human attention
L. Itti and P. Baldi · 2009
Earlier work this paper cites.
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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Probabilistic Graphical Models: Principles and Techniques - Adaptive Computation and Machine Learning
D. Koller and N. Friedman · 2009
Earlier work this paper cites.
Anomaly detection in crowded scenes
V. Mahadevan, W. Li, V. Bhalodia, and N. Vasconcelos · 2010
Earlier work this paper cites.
Detecting anomalies in people’s trajectories using spectral graph analysis
S. Calderara, U. Heinemann, A. Prati, R. Cucchiara, and N. Tishby · 2011
Earlier work this paper cites.
Sparse reconstruction cost for abnormal event detection
Y. Cong, J. Yuan, and J. Liu · 2011
Earlier work this paper cites.
The neural autoregressive distribution estimator
H. Larochelle and I. Murray · 2011
Earlier work this paper cites.
Online detection of unusual events in videos via dynamic sparse coding
B. Zhao, L. Fei-Fei, and E. P. Xing · 2011
Earlier work this paper cites.
Self-organization and associative memory
T. Kohonen · 2012
Earlier work this paper cites.
Novelty or surprise?
A. Barto, M. Mirolli, and G. Baldassarre · 2013
Earlier work this paper cites.
Abnormal event detection at 150 fps in matlab
C. Lu, J. Shi, and J. Jia · 2013
Earlier work this paper cites.
Rnade: The real-valued neural autoregressive density-estimator
B. Uria, I. Murray, and H. Larochelle · 2013
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Auto-encoding variational bayes
D. P. Kingma and M. Welling · 2014
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Anomaly detection and localization in crowded scenes
W. Li, V. Mahadevan, and N. Vasconcelos · 2014
Cited alongside, same era.
A deep and tractable density estimator
B. Uria, I. Murray, and H. Larochelle · 2014
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Visualizing and understanding convolutional networks
M. D. Zeiler and R. Fergus · 2014
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Made: Masked autoencoder for distribution estimation
M. Germain, K. Gregor, I. Murray, and H. Larochelle · 2015
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Joint detection and recounting of abnormal events by learning deep generic knowledge
R. Hinami, T. Mei, and S. Satoh · 2017
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Unmasking the abnormal events in video
R. T. Ionescu, S. Smeureanu, B. Alexe, and M. Popescu · 2017
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Predicting deeper into the future of semantic segmentation
P. Luc, N. Neverova, C. Couprie, J. Verbeek, and Y. LeCun · 2017
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Remembering history with convolutional lstm for anomaly detection
W. Luo, W. Liu, and S. Gao · 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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Curiosity-driven exploration by self-supervised prediction
D. Pathak, P. Agrawal, A. A. Efros, and T. Darrell · 2017
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D. P. Kingma and J. Ba · 2015
Cited alongside, same era.
A unified framework for event summarization and rare event detection from multiple views
J. Kwon and K. M. Lee · 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.
Deep residual learning for image recognition
K. He, X. Zhang, S. Ren, and J. Sun · 2016
Cited alongside, same era.
Wavenet: A generative model for raw audio
A. v. d. Oord, S. Dieleman, H. Zen, K. Simonyan, O. Vinyals, A. Graves, N. Kalchbrenner, A. Senior, and K. Kavukcuoglu · 2016
Cited alongside, same era.
A note on the evaluation of generative models
L. Theis, A. v. d. Oord, and M. Bethge · 2016
Cited alongside, same era.
Training adversarial discriminators for cross-channel abnormal event detection in crowds
M. Ravanbakhsh, E. Sangineto, M. Nabi, and N. Sebe · 2017
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Unsupervised anomaly detection with generative adversarial networks to guide marker discovery
T. Schlegl, P. Seeböck, S. M. Waldstein, U. Schmidt-Erfurth, and G. Langs · 2017
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An empirical evaluation of generic convolutional and recurrent networks for sequence modeling
S. Bai, J. Z. Kolter, and V. Koltun · 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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Predicting the driver’s focus of attention: the dr(eye)ve project
A. Palazzi, D. Abati, S. Calderara, F. Solera, and R. Cucchiara · 2018
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Deep-anomaly: Fully convolutional neural network for fast anomaly detection in crowded scenes
M. Sabokrou, M. Fayyaz, M. Fathy, Z. Moayed, and R. Klette · 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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Vae with a vamp prior
J. M. Tomczak and M. Welling · 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
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Resampled priors for variational autoencoders
M. Bauer and A. Mnih · 2019
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