Augusteijn, M.F.,
Folkert, B.A.,
Neural network classification and novelty detection,
JRS(23), No. 14, July 2002, pp. 2891-2902.
0208
BibRef
Cao, L.J.[Li Juan],
Lee, H.P.[Heow Pueh],
Chong, W.K.[Wai Keong],
Modified support vector novelty detector using training data with
outliers,
PRL(24), No. 14, October 2003, pp. 2479-2487.
Elsevier DOI
0307
BibRef
He, C.[Chao],
Girolami, M.A.[Mark A.],
Novelty detection employing an L2 optimal non-parametric density
estimator,
PRL(25), No. 12, September 2004, pp. 1389-1397.
Elsevier DOI
0409
Reduced set density estimator.
Binary classification.
BibRef
Lee, H.J.[Hyoung-Joo],
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Application of LVQ to novelty detection using outlier training data,
PRL(27), No. 13, 1 October 2006, pp. 1572-1579.
Elsevier DOI Novelty detection; Outlier detection; Novel data; Codebook methods;
Self-organizing maps; Learning vector quantization
0606
BibRef
Camci, F.[Fatih],
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General support vector representation machine for one-class
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PR(41), No. 10, October 2008, pp. 3021-3034.
Elsevier DOI
0808
Novelty detection; One-class classification; Support vector machine;
Non-stationary classes; Non-stationary processes; Online training;
Outlier detection
BibRef
Wu, M.R.[Ming-Rui],
Ye, J.P.[Jie-Ping],
A Small Sphere and Large Margin Approach for Novelty Detection Using
Training Data with Outliers,
PAMI(31), No. 11, November 2009, pp. 2088-2092.
IEEE DOI
0910
BibRef
Quinn, J.A.[John A.],
Williams, C.K.I.[Christopher K.I.],
McIntosh, N.[Neil],
Factorial Switching Linear Dynamical Systems Applied to Physiological
Condition Monitoring,
PAMI(31), No. 9, September 2009, pp. 1537-1551.
IEEE DOI
0907
BibRef
Earlier: A1, A2, Only:
Known Unknowns: Novelty Detection in Condition Monitoring,
IbPRIA07(I: 1-6).
Springer DOI
0706
Kalman filter in time series analysis.
Analysis of systems with unknown factors that switch between states.
ICU monitoring data.
BibRef
Hansen, M.S.[Michael Sass],
Sjostrand, K.[Karl],
Larsen, R.[Rasmus],
On the regularization path of the support vector domain description,
PRL(31), No. 13, 1 October 2010, pp. 1919-1923.
Elsevier DOI
1003
Support vector domain description (SVDD); Regularization path;
One-class classifier; Novelty detection
BibRef
Filippone, M.[Maurizio],
Sanguinetti, G.[Guido],
Information theoretic novelty detection,
PR(43), No. 3, March 2010, pp. 805-814.
Elsevier DOI
1001
Novelty detection; Information theory; Mixture of Gaussians; Density estimation
BibRef
Li, Y.H.[Yu-Hua],
Selecting training points for one-class support vector machines,
PRL(32), No. 11, 1 August 2011, pp. 1517-1522.
Elsevier DOI
1108
One-class support vector machines; Training set selection; Extreme
points; Novelty detection
BibRef
Xiao, Y.C.[Ying-Chao],
Wang, H.G.[Huan-Gang],
Xu, W.L.[Wen-Li],
Zhou, J.[Junwu],
L1 norm based KPCA for novelty detection,
PR(46), No. 1, January 2013, pp. 389-396.
Elsevier DOI
1209
KPCA; L1 norm; Novelty detection
One class classification problem
BibRef
de Morsier, F.,
Tuia, D.,
Borgeaud, M.,
Gass, V.,
Thiran, J.P.,
Semi-Supervised Novelty Detection Using SVM Entire Solution Path,
GeoRS(51), No. 4, April 2013, pp. 1939-1950.
IEEE DOI
1304
BibRef
Jumutc, V.,
Suykens, J.A.K.[Johan A.K.],
Multi-Class Supervised Novelty Detection,
PAMI(36), No. 12, December 2014, pp. 2510-2523.
IEEE DOI
1411
Algorithm design and analysis
BibRef
Rudi, A.[Alessandro],
Odone, F.[Francesca],
de Vito, E.[Ernesto],
Geometrical and computational aspects of Spectral Support Estimation
for novelty detection,
PRL(36), No. 1, 2014, pp. 107-116.
Elsevier DOI
1312
Support estimation
BibRef
Sadooghi, M.S.[Mohammad Saleh],
Khadem, S.E.[Siamak Esmaeilzadeh],
Improving one class support vector machine novelty detection scheme
using nonlinear features,
PR(83), 2018, pp. 14-33.
Elsevier DOI
1808
Novelty detection, OC-SVM, Nonlinear feature, Wavelet,
Bearing vibration signal, Entropy
BibRef
Vo, B.N.[Ba-Ngu],
Dam, N.[Nhan],
Phung, D.[Dinh],
Tran, N.Q.[Nhat-Quang],
Vo, B.T.[Ba-Tuong],
Model-based learning for point pattern data,
PR(84), 2018, pp. 136-151.
Elsevier DOI
1809
BibRef
Earlier: A1, A4, A3, A5, Only:
Model-Based Classification and Novelty Detection for
Point Pattern Data,
ICPR16(2622-2627)
IEEE DOI
1705
BibRef
And: A4, A1, A3, A5, Only:
Clustering for point pattern data,
ICPR16(3174-3179)
IEEE DOI
1705
Point pattern, Point process, Random finite set,
Multiple instance learning, Classification, Novelty detection, Clustering.
Computational modeling, Data models,
Maximum likelihood estimation, Measurement units, Niobium,
Radio frequency, Training data,
multiple instance data, naive Bayes model.
Data models, Feature extraction, Indexes, Measurement,
Clustering, affinity propagation,
expectation-maximization.
BibRef
Mohammadi-Ghazi, R.[Reza],
Marzouk, Y.M.[Youssef M.],
Büyüköztürk, O.[Oral],
Conditional classifiers and boosted conditional Gaussian mixture
model for novelty detection,
PR(81), 2018, pp. 601-614.
Elsevier DOI
1806
Novelty detection, Mixture models, Graphical models,
Conditional dependence, Conditional density,
False positive
BibRef
Zhang, Y.Y.[Ying-Ying],
Gong, Y.X.[Yu-Xin],
Zhu, H.G.[Hao-Gang],
Bai, X.[Xiao],
Tang, W.Z.[Wen-Zhong],
Multi-head enhanced self-attention network for novelty detection,
PR(107), 2020, pp. 107486.
Elsevier DOI
2008
One-class classification, Multihead attention network,
Adversarial-balance loss, Adversarial Learning,
Multihead enhanced self-attention
BibRef
Pang, G.S.[Guan-Song],
Shen, C.H.[Chun-Hua],
Cao, L.B.[Long-Bing],
van den Hengel, A.J.[Anton J.],
Deep Learning for Anomaly Detection: A Review,
Surveys(54), No. 2, March 2021, pp. xx-yy.
DOI Link
2104
Anomaly detection, outlier detection, novelty detection,
one-class classification, deep learning
BibRef
Palacios-Alonso, M.A.[Miguel A.],
Escalante, H.J.[Hugo Jair],
Sucar, L.E.[L. Enrique],
Perceptual and Pixel-Wise Information for Visual Novelty Detection,
MCPR21(251-260).
Springer DOI
2108
BibRef
Park, J.[Jaewoo],
Jung, Y.G.[Yoon Gyo],
Teoh, A.B.J.[Andrew Beng Jin],
Discriminative Multi -level Reconstruction under Compact Latent Space
for One-Class Novelty Detection,
ICPR21(7095-7102)
IEEE DOI
2105
Force measurement, Semantics, Measurement uncertainty, Force,
Extraterrestrial measurements, Data models
BibRef
Kwon, G.,
Prabhushankar, M.,
Temel, D.,
Al Regib, G.,
Novelty Detection Through Model-Based Characterization of Neural
Networks,
ICIP20(3179-3183)
IEEE DOI
2011
Image reconstruction, Neural networks, Training,
Feature extraction, Loss measurement, Backpropagation, Decoding,
Representation learning
BibRef
Oza, P.[Poojan],
Nguyen, H.V.[Hien V.],
Patel, V.M.[Vishal M.],
Multiple Class Novelty Detection Under Data Distribution Shift,
ECCV20(VII:432-449).
Springer DOI
2011
BibRef
Oza, P.[Poojan],
Patel, V.M.[Vishal M.],
Utilizing Patch-level Category Activation Patterns for Multiple Class
Novelty Detection,
ECCV20(X:421-437).
Springer DOI
2011
Novel samples.
BibRef
Bhattacharjee, S.,
Mandal, D.,
Biswas, S.,
Multi-class Novelty Detection Using Mix-up Technique,
WACV20(1389-1398)
IEEE DOI
2006
Training, Testing, Task analysis, Training data, Machine learning,
Predictive models, Image color analysis
BibRef
Abati, D.[Davide],
Porrello, A.[Angelo],
Calderara, S.[Simone],
Cucchiara, R.[Rita],
Latent Space Autoregression for Novelty Detection,
CVPR19(481-490).
IEEE DOI
2002
BibRef
Bhattacharjee, S.[Supritam],
Mudunuri, S.P.[Sivaram P.],
Biswas, S.[Soma],
Do I Know You? A Two-Stage Framework for Novelty Detection,
ICIP19(2536-2540)
IEEE DOI
1910
Does the query belong to a trained class or something else.
Novelty detection, comparator network, Score fusion
BibRef
Bhattacharjee, S.,
Mandal, D.,
Biswas, S.,
Autoencoder based novelty detection for generalized zero shot
learning,
ICIP19(3646-3650)
IEEE DOI
1910
Generalized Zero Shot Learning, Novelty Detection, Autoencoder
BibRef
Perera, P.[Pramuditha],
Nallapati, R.[Ramesh],
Xiang, B.[Bing],
OCGAN: One-Class Novelty Detection Using GANs With Constrained Latent
Representations,
CVPR19(2893-2901).
IEEE DOI
2002
BibRef
Park, S.H.[Seong Hyeon],
Tack, J.[Jihoon],
Heo, B.[Byeongho],
Ha, J.W.[Jung-Woo],
Shin, J.[Jinwoo],
K-centered Patch Sampling for Efficient Video Recognition,
ECCV22(XXXV:160-176).
Springer DOI
2211
BibRef
Lee, K.[Kibok],
Lee, K.[Kimin],
Min, K.[Kyle],
Zhang, Y.T.[Yu-Ting],
Shin, J.[Jinwoo],
Lee, H.L.[Hong-Lak],
Hierarchical Novelty Detection for Visual Object Recognition,
CVPR18(1034-1042)
IEEE DOI
1812
Find closest superclass of novel object.
Taxonomy, Task analysis, Cats, Training, Semantics, Object recognition
BibRef
Aitchison, M.,
Green, R.,
Novelty Detection in Thermal Video,
IVCNZ18(1-6)
IEEE DOI
1902
Training, Neural networks, Estimation, Measurement, Birds, Rats,
Manifolds, Deep Neural Networks (DNN), Novelty Detection, Density Estimation
BibRef
Schultheiss, A.[Alexander],
Käding, C.[Christoph],
Freytag, A.[Alexander],
Denzler, J.[Joachim],
Finding the Unknown: Novelty Detection with Extreme Value Signatures of
Deep Neural Activations,
GCPR17(226-238).
Springer DOI
1711
Which level of CNN has extreme values.
BibRef
Bodesheim, P.[Paul],
Freytag, A.[Alexander],
Rodner, E.[Erik],
Denzler, J.[Joachim],
Local Novelty Detection in Multi-class Recognition Problems,
WACV15(813-820)
IEEE DOI
1503
Computational modeling
BibRef
Bodesheim, P.[Paul],
Freytag, A.[Alexander],
Rodner, E.[Erik],
Kemmler, M.[Michael],
Denzler, J.[Joachim],
Kernel Null Space Methods for Novelty Detection,
CVPR13(3374-3381)
IEEE DOI
1309
kernel methods. Finding unknown objects.
BibRef
k
Chapter on Motion -- Human Motion, Surveillance, Tracking, Surveillance, Activities continues in
Human Motion Understanding and Analysis .