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Novelty detection is the process of identifying the observation(s) that differ in some respect from the training observations (the target class).
Network constraints and multi-objective optimization for one-class classification
M. M. Moya and D. R. Hush · 1996
Earlier work this paper cites.
Lof: identifying density-based local outliers
M. M. Breunig, H.-P. Kriegel, R. T. Ng, and J. Sander · 2000
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Anomaly detection over noisy data using learned probability distributions
E. Eskin · 2000
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Novelty detection: a review—part 1: statistical approaches
M. Markou and S. Singh · 2003
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On-line unsupervised outlier detection using finite mixtures with discounting learning algorithms
K. Yamanishi, J.-I. Takeuchi, G. Williams, and P. Milne · 2004
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A non-local algorithm for image denoising
A. Buades, B. Coll, and J.-M. Morel · 2005
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Pattern recognition and machine learning
C. M. Bishop · 2006
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One-class novelty detection for seizure analysis from intracranial eeg
A. B. Gardner, A. M. Krieger, G. Vachtsevanos, and B. Litt · 2006
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Outlier detection using random walks
H. Moonesignhe and P.-N. Tan · 2006
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Detecting irregularities in images and in video
O. Boiman and M. Irani · 2007
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Caltech-256 object category dataset
G. Griffin, A. Holub, and P. Perona · 2007
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Ucsd pedestrian dataset
A. Chan and N. Vasconcelos · 2008
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Extracting and composing robust features with denoising autoencoders
P. Vincent, H. Larochelle, Y. Bengio, and P.-A. Manzagol · 2008
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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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Mnist handwritten digit database
Y. LeCun, C. Cortes, and C. J. Burges · 2010
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Robust subspace segmentation by low-rank representation
G. Liu, Z. Lin, and Y. Yu · 2010
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Anomaly detection in crowded scenes
V. Mahadevan, W. Li, V. Bhalodia, and N. Vasconcelos · 2010
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Robust pca via outlier pursuit
H. Xu, C. Caramanis, and S. Sanghavi · 2010
Cited alongside, same era.
Sparse reconstruction cost for abnormal event detection
Y. Cong, J. Yuan, and J. Liu · 2011
Cited alongside, same era.
Trajectory learning for activity understanding: Unsupervised, multilevel, and long-term adaptive approach
B. T. Morris and M. M. Trivedi · 2011
Cited alongside, same era.
Multi-scale and real-time non-parametric approach for anomaly detection and localization
M. Bertini, A. Del Bimbo, and L. Seidenari · 2012
Cited alongside, same era.
Generative adversarial nets
I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio · 2014
Cited alongside, same era.
One-class classification: taxonomy of study and review of techniques
S. S. Khan and M. G. Madden · 2014
Cited alongside, same era.
Learning discriminative reconstructions for unsupervised outlier removal
Y. Xia, X. Cao, F. Wen, G. Hua, and J. Sun · 2015
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Learning deep representations of appearance and motion for anomalous event detection
D. Xu, E. Ricci, Y. Yan, J. Song, and N. Sebe · 2015
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Tensorflow: Large-scale machine learning on heterogeneous distributed systems
M. Abadi, A. Agarwal, P. Barham, E. Brevdo, Z. Chen, C. Citro, G. S. Corrado, A. Davis, J. Dean, M. Devin, et al · 2016
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Image-to-image translation with conditional adversarial networks
P. Isola, J.-Y. Zhu, T. Zhou, and A. A. Efros · 2016
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Coherence pursuit: Fast, simple, and robust principal component analysis
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Anomaly detection and localization in crowded scenes
W. Li, V. Mahadevan, and N. Vasconcelos · 2014
Cited alongside, same era.
Very deep convolutional networks for large-scale image recognition
K. Simonyan and A. Zisserman · 2014
Cited alongside, same era.
Video anomaly detection based on a hierarchical activity discovery within spatio-temporal contexts
D. Xu, R. Song, X. Wu, N. Li, W. Feng, and H. Qian · 2014
Cited alongside, same era.
Robust feature-sample linear discriminant analysis for brain disorders diagnosis
E. Adeli, K.-H. Thung, L. An, F. Shi, and D. Shen · 2015
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.
Robust computation of linear models by convex relaxation
G. Lerman, M. B. McCoy, J. A. Tropp, and T. Zhang · 2015
Cited alongside, same era.
M. Rahmani and G. Atia · 2016
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Video anomaly detection and localisation based on the sparsity and reconstruction error of auto-encoder
M. Sabokrou, M. Fathy, and M. Hoseini · 2016
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Improved techniques for training gans
T. Salimans, I. Goodfellow, W. Zaremba, V. Cheung, A. Radford, and X. Chen · 2016
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Wasserstein generative adversarial networks
M. Arjovsky, S. Chintala, and L. Bottou · 2017
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Image denoising via cnns: An adversarial approach
N. Divakar and R. V. Babu · 2017
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Finding anomalies with generative adversarial networks for a patrolbot
W. Lawson, E. Bekele, and K. Sullivan · 2017
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Incremental kernel null space discriminant analysis for novelty detection
J. Liu, Z. Lian, Y. Wang, and J. Xiao · 2017
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Abnormal event detection in videos using generative adversarial nets
M. Ravanbakhsh, M. Nabi, E. Sangineto, L. Marcenaro, C. Regazzoni, and N. Sebe · 2017
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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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Deep-cascade: Cascading 3d deep neural networks for fast anomaly detection and localization in crowded scenes
M. Sabokrou, M. Fayyaz, M. Fathy, and R. Klette · 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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Provable self-representation based outlier detection in a union of subspaces
C. You, D. P. Robinson, and R. Vidal · 2017
Later among the works it cites.