14.3.2 Outlier Detection and Analysis

Rousseeuw, P.J.,
Robust Regression and Outlier Detection,
John Wiley&Sons, New York, 1987. BibRef 8700

Rousseeuw, P.J.,
Least Median of Squares Regression,
ASAJ(79), 1984, pp. 871-880. BibRef 8400

Kharin, Y.[Yurij], Zhuk, E.[Eugene],
Filtering of multivariate samples containing 'outliers' for clustering,
PRL(19), No. 12, 30 October 1998, pp. 1077-1085. BibRef 9810
Earlier:
Robustness in statistical pattern recognition under 'contaminations' of training samples,
ICPR94(B:504-506).
IEEE DOI 9410
BibRef

Jiang, M.F., Tseng, S.S., Su, C.M.,
Two-phase clustering process for outliers detection,
PRL(22), No. 6-7, May 2001, pp. 691-700.
Elsevier DOI 0105
BibRef

Ramaswamy, S.[Sridhar], Rastogi, R.[Rajeev], Shim, K.[Kyuseok],
Efficient algorithms for mining outliers from large data sets,
ACM SIGMOD(29), No. 2, June 2000, pp. 427-438.
WWW Link. Formulation for distance based outliers. BibRef 0006

He, Z.Y.[Zeng-You], Xu, X.F.[Xiao-Fei], Deng, S.C.[Sheng-Chun],
Discovering cluster-based local outliers,
PRL(24), No. 9-10, June 2003, pp. 1641-1650.
Elsevier DOI 0304
BibRef

Shekhar, S.[Shashi], Lu, C.T.[Chang-Tien], Zhang, P.S.[Pu-Sheng],
A Unified Approach to Detecting Spatial Outliers,
GeoInfo(7), No. 2, June 2003, pp. 139-166.
DOI Link 0307
BibRef

Hu, T.M.[Tian-Ming], Sung, S.Y.[Sam Y.],
Detecting pattern-based outliers,
PRL(24), No. 16, December 2003, pp. 3059-3068.
Elsevier DOI 0310
BibRef

Zhang, J.S.[Jiang-She], Leung, Y.W.[Yiu-Wing],
Robust clustering by pruning outliers,
SMC-B(33), No. 6, December 2003, pp. 983-999.
IEEE Abstract. 0401
BibRef

Kim, J.H.[Jae-Hak], Han, J.H.[Joon H.],
Outlier correction from uncalibrated image sequence using the Triangulation method,
PR(39), No. 3, March 2006, pp. 394-404.
Elsevier DOI 0601
BibRef

Hautamaki, V., Karkkainen, I., Franti, P.,
Outlier detection using k-nearest neighbour graph,
ICPR04(III: 430-433).
IEEE DOI 0409
BibRef

Bandyopadhyay, S.[Sanghamitra], Santra, S.[Santanu],
A genetic approach for efficient outlier detection in projected space,
PR(41), No. 4, April 2008, pp. 1338-1349.
Elsevier DOI 0801
Deviation detection; Gene expression; Genetic algorithm; Grid count tree; Projected dimension; Outlier BibRef

Zhang, J.F.[Ji-Fu], Jiang, Y.Y.[Yi-Yong], Chang, K.H.[Kai H.], Zhang, S.[Sulan], Cai, J.H.[Jiang-Hui], Hu, L.H.[Li-Hua],
A concept lattice based outlier mining method in low-dimensional subspaces,
PRL(30), No. 15, 1 November 2009, pp. 1434-1439.
Elsevier DOI 0910
Outliers; Concept lattice; Sparsity coefficient; Density coefficient; Intent reduction BibRef

Chen, Y.X.[Yi-Xin], Dang, X.[Xin], Peng, H.X.[Han-Xiang], Bart, Jr., H.L.[Henry L.],
Outlier Detection with the Kernelized Spatial Depth Function,
PAMI(31), No. 2, February 2009, pp. 288-305.
IEEE DOI 0901
Outliers in input data. BibRef

Szeto, C.C.[Chi-Cheong], Hung, E.[Edward],
Mining outliers with faster cutoff update and space utilization,
PRL(31), No. 11, 1 August 2010, pp. 1292-1301.
Elsevier DOI 1008
Outlier detection; Distance-based outliers; Disk-based algorithms; Memory optimization
See also Efficient algorithms for mining outliers from large data sets. BibRef

Jiang, F.[Feng], Sui, Y.F.[Yue-Fei], Cao, C.[Cungen],
A hybrid approach to outlier detection based on boundary region,
PRL(32), No. 14, 15 October 2011, pp. 1860-1870.
Elsevier DOI 1110
Outlier detection; Rough sets; Boundary; Distance; KDD BibRef

Daneshpazhouh, A.[Armin], Sami, A.[Ashkan],
Entropy-based outlier detection using semi-supervised approach with few positive examples,
PRL(49), No. 1, 2014, pp. 77-84.
Elsevier DOI 1410
Data mining BibRef

Rasheed, F., Alhajj, R.,
A Framework for Periodic Outlier Pattern Detection in Time-Series Sequences,
Cyber(44), No. 5, May 2014, pp. 569-582.
IEEE DOI 1405
data mining BibRef

Ru, X.H.[Xiao-Hu], Liu, Z.[Zheng], Huang, Z.T.[Zhi-Tao], Jiang, W.L.[Wen-Li],
Normalized residual-based constant false-alarm rate outlier detection,
PRL(69), No. 1, 2016, pp. 1-7.
Elsevier DOI 1601
Outlier detection BibRef

Domingues, R.[Rémi], Filippone, M.[Maurizio], Michiardi, P.[Pietro], Zouaoui, J.[Jihane],
A comparative evaluation of outlier detection algorithms: Experiments and analyses,
PR(74), No. 1, 2018, pp. 406-421.
Elsevier DOI 1711
Outlier detection BibRef

Xu, Z.[Zhi], Cai, G.Y.[Guo-Yong], Wen, Y.M.[Yi-Min], Chen, D.D.[Dong-Dong], Han, L.Y.[Li-Yao],
Image set-based classification using collaborative exemplars representation,
SIViP(12), No. 4, May 2018, pp. 607-615.
Springer DOI 1805
Represent the image sets and deal with outliers. BibRef

Ning, J.[Jin], Chen, L.[Leiting], Zhou, C.[Chuan], Wen, Y.[Yang],
Parameter k search strategy in outlier detection,
PRL(112), 2018, pp. 56-62.
Elsevier DOI 1809
Parameter k, Outlier detection, Mutual neighbor graph BibRef

Chakraborty, D.[Debasrita], Narayanan, V.[Vaasudev], Ghosh, A.[Ashish],
Integration of deep feature extraction and ensemble learning for outlier detection,
PR(89), 2019, pp. 161-171.
Elsevier DOI 1902
Deep learning, Autoencoders, Probabilistic neural networks, Ensemble learning, Outlier detection BibRef

Riani, M.[Marco], Atkinson, A.C.[Anthony C.], Cerioli, A.[Andrea], Corbellini, A.[Aldo],
Efficient robust methods via monitoring for clustering and multivariate data analysis,
PR(88), 2019, pp. 246-260.
Elsevier DOI 1901
Bovine phlegmon, Car-bike plot, Clustering, Eigenvalue constraint, Forward search, MCD, MM-Estimation, Outliers BibRef

Dutta, J.K.[Jayanta K.], Banerjee, B.[Bonny],
Improved outlier detection using sparse coding-based methods,
PRL(122), 2019, pp. 99-105.
Elsevier DOI 1904
Outlier detection, Outlier scoring, High dimension, Difficulty level BibRef

Blouvshtein, L.[Leonid], Cohen-Or, D.[Daniel],
Outlier Detection for Robust Multi-Dimensional Scaling,
PAMI(41), No. 9, Sep. 2019, pp. 2273-2279.
IEEE DOI 1908
Image edge detection, Histograms, Robustness, Data visualization, Distortion, Tuning, Cognition, Multidimensional scaling, outliers, data visualization BibRef

Slavakis, K.[Konstantinos], Banerjee, S.[Sinjini],
Robust Hierarchical-Optimization RLS Against Sparse Outliers,
SPLetters(27), 2020, pp. 171-175.
IEEE DOI 2002
Recursive Least Squares. RLS, robust, outliers, sparsity BibRef

Rofatto, V.F.[Vinicius Francisco], Matsuoka, M.T.[Marcelo Tomio], Klein, I.[Ivandro], Veronez, M.R.[Maurício Roberto], da Silveira, L.G.[Luiz Gonzaga],
A Monte Carlo-Based Outlier Diagnosis Method for Sensitivity Analysis,
RS(12), No. 5, 2020, pp. xx-yy.
DOI Link 2003
IDS: Iterative Data Snooping. BibRef

Goh, M.J.S.[Michael Joon Seng], Chiew, Y.S.[Yeong Shiong], Foo, J.J.[Ji Jinn],
Outlier percentage estimation for shape- and parameter-independent outlier detection,
IET-IPR(14), No. 14, December 2020, pp. 3414-3421.
DOI Link 2012
BibRef

Traun, C.[Christoph], Schreyer, M.L.[Manuela Larissa], Wallentin, G.[Gudrun],
Empirical Insights from a Study on Outlier Preserving Value Generalization in Animated Choropleth Maps,
IJGI(10), No. 4, 2021, pp. xx-yy.
DOI Link 2104
BibRef

Mukhriya, A.[Akanksha], Kumar, R.[Rajeev],
Building outlier detection ensembles by selective parameterization of heterogeneous methods,
PRL(146), 2021, pp. 126-133.
Elsevier DOI 2105
Outlier detection, Ensemble learning, Member selection, Parameterization, Accuracy-diversity trade-off BibRef

Chong, P.[Penny], Cheung, N.M.[Ngai-Man], Elovici, Y.[Yuval], Binder, A.[Alexander],
Toward Scalable and Unified Example-Based Explanation and Outlier Detection,
IP(31), 2022, pp. 525-540.
IEEE DOI 2112
Prototypes, Training, Anomaly detection, Task analysis, Feature extraction, Predictive models, Kernel, Prototypes, image classification BibRef

Ge, H.M.[Hai-Miao], Wang, L.G.[Li-Guo], Pan, H.Z.[Hai-Zhu], Zhu, Y.X.[Yue-Xia], Zhao, X.Y.[Xiao-Yu], Liu, M.[Moqi],
Affinity Propagation Based on Structural Similarity Index and Local Outlier Factor for Hyperspectral Image Clustering,
RS(14), No. 5, 2022, pp. xx-yy.
DOI Link 2203
BibRef

Sedghi, M.[Mahlagha], Georgiopoulos, M.[Michael], Atia, G.K.[George K.],
Sketches by MoSSaRT: Representative selection from manifolds with gross sparse corruptions,
PR(124), 2022, pp. 108454.
Elsevier DOI 2203
Data selection. Representative selection, Gross sparse corruption, Manifold learning, Reproducing kernel Hilbert spaces BibRef

Yuan, L.X.[Li-Xin], Yang, G.Q.[Guo-Qiang], Xu, Q.[Qian], Lu, T.[Tong],
Discriminative feature selection with directional outliers correcting for data classification,
PR(126), 2022, pp. 108541.
Elsevier DOI 2204
Feature selection, Directional outlier, Redundant features, Deviation, Supervised method BibRef

Liu, Q.[Qi], Li, X.P.[Xiao-Peng], Cao, H.[Hui], Wu, Y.T.[Yun-Tao],
From Simulated to Visual Data: A Robust Low-Rank Tensor Completion Approach Using L_p-Regression for Outlier Resistance,
CirSysVideo(32), No. 6, June 2022, pp. 3462-3474.
IEEE DOI 2206
Tensors, Matrix decomposition, Minimization, Noise reduction, Data models, Correlation, Computational modeling, color image inpainting and denoising BibRef

Huyan, N.[Ning], Quan, D.[Dou], Zhang, X.R.[Xiang-Rong], Liang, X.F.[Xue-Feng], Chanussot, J.[Jocelyn], Jiao, L.C.[Li-Cheng],
Unsupervised Outlier Detection Using Memory and Contrastive Learning,
IP(31), 2022, pp. 6440-6454.
IEEE DOI 2211
Feature extraction, Prototypes, Image reconstruction, Training, Memory modules, Anomaly detection, Detectors, Anomaly detection, unsupervised learning BibRef

Tan, X.[Xu], Yang, J.W.[Jia-Wei], Rahardja, S.[Susanto],
Sparse random projection isolation forest for outlier detection,
PRL(163), 2022, pp. 65-73.
Elsevier DOI 2212
Outlier detection, Anomaly detection, Isolation forest, Random projection, Sparse random projection BibRef

Bao, J.F.[Jun-Fang], Li, J.L.[Jian-Li], Wei, M.D.[Meng-Di], Qu, C.Y.[Chun-Yu],
An Improved Innovation Robust Outliers Detection Method for Airborne Array Position and Orientation Measurement System,
RS(15), No. 1, 2023, pp. xx-yy.
DOI Link 2301
BibRef

Wang, S.Q.[Si-Qi], Zeng, Y.J.[Yi-Jie], Yu, G.[Guang], Cheng, Z.[Zhen], Liu, X.W.[Xin-Wang], Zhou, S.[Sihang], Zhu, E.[En], Kloft, M.[Marius], Yin, J.P.[Jian-Ping], Liao, Q.[Qing],
E3 Outlier: a Self-Supervised Framework for Unsupervised Deep Outlier Detection,
PAMI(45), No. 3, March 2023, pp. 2952-2969.
IEEE DOI 2302
Task analysis, Self-supervised learning, Anomaly detection, Visualization, Uncertainty, Data models, Measurement uncertainty, unsupervised learning BibRef

Yang, H.[Heng], Carlone, L.[Luca],
Certifiably Optimal Outlier-Robust Geometric Perception: Semidefinite Relaxations and Scalable Global Optimization,
PAMI(45), No. 3, March 2023, pp. 2816-2834.
IEEE DOI 2302
Estimation, Optimization, Programming, Costs, Robot sensing systems, Pose estimation, Standards, Certifiable algorithms, large-scale convex optimization BibRef

Li, F.[Feiran], Fujiwara, K.[Kent], Okura, F.[Fumio], Matsushita, Y.[Yasuyuki],
Shuffled Linear Regression with Outliers in Both Covariates and Responses,
IJCV(131), No. 3, March 2023, pp. 732-751.
Springer DOI 2302
BibRef

Huang, Y.[Yi], Li, Y.[Ying], Jourjon, G.[Guillaume], Seneviratne, S.[Suranga], Thilakarathna, K.[Kanchana], Cheng, A.[Adriel], Webb, D.[Darren], Xu, R.Y.D.[Richard Yi Da],
Calibrated reconstruction based adversarial autoencoder model for novelty detection,
PRL(169), 2023, pp. 50-57.
Elsevier DOI 2305
Novelty detection, Reconstruction, Autoencoder, Calibration BibRef

Mishra, G.[Gargi], Kumar, R.[Rajeev],
An individual fairness based outlier detection ensemble,
PRL(171), 2023, pp. 76-83.
Elsevier DOI 2306
Outlier detection, Ensembles, Individual fairness, Member selection, Performance-fairness trade-off BibRef

Yang, J.W.[Jia-Wei], Tan, X.[Xu], Rahardja, S.[Sylwan],
Outlier detection: How to Select k for k-nearest-neighbors-based outlier detectors,
PRL(174), 2023, pp. 112-117.
Elsevier DOI 2310
Outlier detection, -nearest neighbors, -NN, Neighborhood-based outlier detectors, KFC, neighborhood consistency BibRef

Chen, Q.[Qiong], Xie, L.[Liangru], Zeng, L.R.[Li-Rong], Jiang, S.[Sining], Ding, W.P.[Wei-Ping], Huang, X.M.[Xiao-Meng], Wang, H.[Hao],
Neighborhood Rough Residual Network-Based Outlier Detection Method in IoT-Enabled Maritime Transportation Systems,
ITS(24), No. 11, November 2023, pp. 11800-11811.
IEEE DOI 2311
BibRef

Wu, A.[Aming], Deng, C.[Cheng],
TIB: Detecting Unknown Objects via Two-Stream Information Bottleneck,
PAMI(46), No. 1, January 2024, pp. 611-625.
IEEE DOI 2312
detect unknown objects without the reliance on an auxiliary datase. BibRef

Wang, Y.[Yinan], Sun, W.B.[Wen-Bo], Jin, J.[Jionghua], Kong, Z.Y.[Zhen-Yu], Yue, X.W.[Xiao-Wei],
WOOD: Wasserstein-Based Out-of-Distribution Detection,
PAMI(46), No. 2, February 2024, pp. 944-956.
IEEE DOI 2401
BibRef

Peng, X.[Xi], Qiao, F.C.[Feng-Chun], Zhao, L.[Long],
Out-of-Domain Generalization From a Single Source: An Uncertainty Quantification Approach,
PAMI(46), No. 3, March 2024, pp. 1775-1787.
IEEE DOI 2402
Training, Uncertainty, Task analysis, Adaptation models, Transportation, Robustness, Perturbation methods, uncertainty quantification BibRef

Zhu, F.[Fei], Zhang, X.Y.[Xu-Yao], Cheng, Z.[Zhen], Liu, C.L.[Cheng-Lin],
Revisiting Confidence Estimation: Towards Reliable Failure Prediction,
PAMI(46), No. 5, May 2024, pp. 3370-3387.
IEEE DOI 2404
Deal with overconfident classification. Calibration, Estimation, Reliability, Predictive models, Training, Task analysis, Machine learning, Confidence estimation, flat minima BibRef

Kim, D.W.[Dong-Wook], Park, J.[Juyeon], Chung, H.C.[Hee Cheol], Jeong, S.[Seonghyun],
Unsupervised outlier detection using random subspace and subsampling ensembles of Dirichlet process mixtures,
PR(156), 2024, pp. 110846.
Elsevier DOI 2408
Anomaly detection, Gaussian mixture models, Outlier ensembles, Random projection, Variational inference BibRef

Liu, H.W.[Hua-Wen], Zhang, S.C.[Shi-Chao], Wu, Z.D.[Zong-Da], Li, X.L.[Xue-Long],
Outlier detection using local density and global structure,
PR(157), 2025, pp. 110947.
Elsevier DOI 2409
Outlier detection, Potential energy, Data density, Data hub, Random walk BibRef

Zhu, Y.[Yao], Ma, J.C.[Jia-Cheng], Sun, J.C.[Jia-Cheng], Chen, Z.W.[Ze-Wei], Jiang, R.X.[Rong-Xin], Chen, Y.W.[Yao-Wu], Li, Z.G.[Zhen-Guo],
Towards Understanding the Generative Capability of Adversarially Robust Classifiers,
ICCV21(7708-7717)
IEEE DOI 2203
Training, Image synthesis, Computational modeling, Robustness, Data models, Optimization, Adversarial learning, Neural generative models BibRef

Chang, S.Y.[Shih Yu], Wu, H.C.[Hsiao-Chun],
Random Tensor Analysis: Outlier Detection and Sample-Size Determination,
SPLetters(31), 2024, pp. 2835-2839.
IEEE DOI 2411
Tensors, Anomaly detection, Eigenvalues and eigenfunctions, Vectors, Signal processing, Tail, Linear matrix inequalities, tensor data BibRef

Mukhriya, A.[Akanksha], Kumar, R.[Rajeev],
Iterative target updation based boosting ensembles for outlier detection,
PR(158), 2025, pp. 111023.
Elsevier DOI 2411
Outlier detection, Ensembles, Target formation, Boosting BibRef

Cheng, S.T.[Shi-Tong], Su, X.Y.[Xin-Yu], Chen, B.[Baiyang], Chen, H.M.[Hong-Mei], Peng, D.Z.[De-Zhong], Yuan, Z.[Zhong],
GBMOD: A granular-ball mean-shift outlier detector,
PR(159), 2025, pp. 111115.
Elsevier DOI 2412
Outlier detection, Anomaly detection, Granular-ball computing, Mean-shift, k-nearest neighbor BibRef

Liu, X.Z.[Xin-Ze], Yang, X.J.[Xiao-Jun], Zhang, J.[Jiale], Wang, J.[Jing], Nie, F.P.[Fei-Ping],
Outlier Indicator Based Projection Fuzzy K-Means Clustering for Hyperspectral Image,
SPLetters(32), 2025, pp. 496-500.
IEEE DOI 2501
Hyperspectral imaging, Optimization, Noise, Vectors, Clustering algorithms, Robustness, Linear programming BibRef

Domingos, J.[João], Xavier, J.[João],
Outlier-Resilient Model Fitting via Percentile Losses: Methods for General and Convex Residuals,
SPLetters(32), 2025, pp. 931-935.
IEEE DOI 2503
Vectors, Fitting, Data models, Optimization, Linear programming, Training, Reactive power, Indexes, Estimation, Data mining, subset sampling BibRef

Tan, X.[Xu], Yang, J.W.[Jia-Wei], Chen, J.Q.[Jun-Qi], Rahardja, S.[Sylwan], Rahardja, S.[Susanto],
MSS-PAE: Saving Autoencoder-based Outlier Detection from Unexpected Reconstruction,
PR(163), 2025, pp. 111467.
Elsevier DOI 2503
Outlier detection, Autoencoder, Uncertainty estimation, Mean-shift BibRef

Zhang, Z.M.[Zhe-Min], Gong, X.[Xun],
Generating Multi-Center Classifier via Conditional Gaussian Distribution,
SPLetters(32), 2025, pp. 2030-2034.
IEEE DOI 2505
I.e. items in different poses. Training, Gaussian distribution, Feature extraction, Standards, Posterior probability, Vectors, Image classification, multi-center classifier BibRef

Hu, Q.[Qian], Yuan, Z.[Zhong], Zhang, J.[Jun], Mi, J.[Jusheng],
Fuzzy rough guided subspace anomaly detection in nominal data,
PR(174), 2026, pp. 113024.
Elsevier DOI Code:
WWW Link. 2602
Outlier detection, Anomaly detection, Fuzzy rough sets, Subspace selection, Nominal data, BibRef

Xing, L.[Lei], Liu, Y.F.[Yu-Fei], Xu, L.H.[Lin-Hai], Chen, B.D.[Ba-Dong],
Outlier-robust learning with continuously differentiable least trimmed squares,
PR(175), 2026, pp. 113099.
Elsevier DOI 2603
Robust estimation, Least trimmed squares (LTS), Continuously differentiable LTS (CD-LTS) BibRef

Wang, R.X.[Rong-Xiang], Wan, J.H.[Ji-Hong], Li, X.P.[Xiao-Ping], Tan, S.S.[Shuai-Shuai],
Fast and robust outlier detection: A granular-ball center isolation and region consistency approach,
PR(176), 2026, pp. 113212.
Elsevier DOI 2603
Outlier detection, Granular-ball computing, -nearest neighbor, Isolation BibRef

Zhang, Z.P.[Zhong-Ping], Gao, X.Z.[Xiao-Zhe], Li, S.[Sen],
DDOF: A high-dimensional outlier detection algorithm based on deviation distance outlier factor,
PRL(203), 2026, pp. 126-132.
Elsevier DOI 2604
Data mining, Outlier detection, High-dimensional data, Z-curve, K nearest neighbors, Outlier factor BibRef

Li, Y.H.[Yan-Hua], Ouyang, X.C.[Xiao-Cao], Zhang, J.[Jie], Pan, C.F.[Chao-Fan], Ren, L.F.[Ling-Fei], Yang, X.[Xin],
Beneficial noise learning for open intent classification via granular-ball representation,
PR(177), 2026, pp. 113283.
Elsevier DOI Code:
WWW Link. 2605
Beneficial noise learning, Granular-ball computing, Open intent classification BibRef

Li, J.H.[Jin-Hai], Shi, J.Y.[Jia-Yao], Li, S.[Shen], Chen, Y.[Yu],
Dual-aspect synergistic outlier detection with structural deviation and attribute rarity,
PR(180), 2026, pp. 114084.
Elsevier DOI 2608
Outlier detection, Granular computing, Formal concept analysis, Core concept BibRef

Blanco, V.[Víctor], Espejo, I.[Inmaculada], Páez, R.[Raúl], Rodríguez-Chía, A.M.[Antonio M.],
A mathematical optimization approach to multisphere support vector data description,
PR(180), 2026, pp. 114292.
Elsevier DOI 2608
Mathematical optimization, Outlier detection, Machine learning, Support vector data description, Multiple hyperspheres, Kernels BibRef

Su, X.[Xinyu], Yuan, Z.[Zhong], Huang, W.[Wei], Chen, H.M.[Hong-Mei], Li, Z.[Zheng],
Fuzzy combination entropy-based outlier detector for heterogeneous data,
PR(180), 2026, pp. 114407.
Elsevier DOI 2609
Granular computing, Fuzz combination entropy, Outlier detection, Heterogeneous data BibRef

Liu, Z.H.[Zhong-Hang], Zhou, K.[Kun], Wang, C.S.[Chang-Shuo], Lin, W.Y.[Wen-Yan], Lu, J.B.[Jiang-Bo],
FlexUOD: The Answer to Real-world Unsupervised Image Outlier Detection,
CVPR25(15183-15193)
IEEE DOI 2508
Estimation, Visual systems, Benchmark testing, Robustness, Contamination, Anomaly detection, unsupervised, image, real-world BibRef

Pal, J.B.[Jimut B.], Welling, S.[Shantanu], Saini, H.[Himali], Awate, S.P.[Suyash P.],
Reviving Poor Object Segmentations in OOD Medical Images using Variational-Deep-PCA Modeling on Segmentation Maps with Sampling-Free Learning,
WACV25(9364-9373)
IEEE DOI 2505
Geometry, Image segmentation, Uncertainty, Protocols, Computational modeling, Estimation, Object segmentation, human in the loop BibRef

Buschmann, B.[Benno], Dogaru, A.[Andreea], Eisemann, E.[Elmar], Weinmann, M.[Michael], Egger, B.[Bernhard],
Ranrac: Robust Neural Scene Representations via Random Ray Consensus,
ECCV24(LXXVI: 126-143).
Springer DOI 2412
RANdom RAy Consensus. BibRef

Liu, Z.H.[Zhong-Hang], Lu, P.Z.[Pan-Zhong], Xie, G.Y.[Guo-Yang], Lu, Z.C.[Zhi-Chao], Lin, W.Y.[Wen-Yan],
Rethinking Unsupervised Outlier Detection via Multiple Thresholding,
ECCV24(XVIII: 258-275).
Springer DOI 2412
Code:
WWW Link. BibRef

Salehi, M.[Mohammadreza], Apostolikas, N.[Nikolaos], Gavves, E.[Efstratios], Snoek, C.G.M.[Cees G. M.], Asano, Y.M.[Yuki M.],
Redefining Normal: A Novel Object-level Approach for Multi-object Novelty Detection,
ACCV24(VI: 445-461).
Springer DOI 2412
BibRef

Yavuz, M.[Misra], Güney, F.[Fatma],
O1O: Grouping of Known Classes to Identify Unknown Objects as Odd-one-Out,
ACCV24(X: 394-410).
Springer DOI 2412
BibRef

Chung, H.J.[Hyung-Jin], Ye, J.C.[Jong Chul],
Deep Diffusion Image Prior for Efficient OOD Adaptation in 3d Inverse Problems,
ECCV24(LXXV: 432-455).
Springer DOI 2412
BibRef

An, W.Z.[Wei-Zhi], Zhong, W.L.[Wen-Liang], Jiang, F.[Feng], Ma, H.[Hehuan], Huang, J.Z.[Jun-Zhou],
Causal Subgraphs and Information Bottlenecks: Redefining OOD Robustness in Graph Neural Networks,
ECCV24(LXXXVIII: 473-489).
Springer DOI 2412
BibRef

Liu, J.H.[Jia-Hui], Wen, X.[Xin], Zhao, S.Z.[Shi-Zhen], Chen, Y.X.[Ying-Xian], Qi, X.J.[Xiao-Juan],
Can OOD Object Detectors Learn from Foundation Models?,
ECCV24(XII: 213-231).
Springer DOI 2412
BibRef

Dereka, S.[Stanislav], Karpukhin, I.[Ivan], Zhdanov, M.[Maksim], Kolesnikov, S.[Sergey],
Diversifying Deep Ensembles: A Saliency Map Approach for Enhanced OOD Detection, Calibration, and Accuracy,
ICIP24(437-443)
IEEE DOI 2411
Training, Accuracy, Estimation, Benchmark testing, Predictive models, Calibration, Ensemble diversity, OOD detection, calibration, neural networks BibRef

Noohdani, F.H.[Fahimeh Hosseini], Hosseini, P.[Parsa], Parast, A.Y.[Aryan Yazdan], Araghi, H.Y.[Hamidreza Yaghoubi], Baghshah, M.S.[Mahdieh Soleymani],
Decompose-and-Compose: A Compositional Approach to Mitigating Spurious Correlation,
CVPR24(27652-27661)
IEEE DOI Code:
WWW Link. 2410
Training, Correlation, Accuracy, Risk minimization, Diversity reception, Training data, Robustness, Spurious Correlation BibRef

Albiero, V.[Vítor], Mehta, R.[Raghav], Evtimov, I.[Ivan], Bell, S.[Samuel], Sagun, L.[Levent], Markosyan, A.[Aram],
Confusing Large Models by Confusing Small Models,
OutDistri23(4306-4314)
IEEE DOI 2401
BibRef

Jeon, M.[Myeongho], Kang, M.[Myungjoo], Lee, J.[Joonseok],
A Unified Framework for Robustness on Diverse Sampling Errors,
ICCV23(1464-1472)
IEEE DOI 2401
BibRef

Narayanaswamy, V.[Vivek], Mubarka, Y.[Yamen], Anirudh, R.[Rushil], Rajan, D.[Deepta], Thiagarajan, J.J.[Jayaraman J.],
Exploring Inlier and Outlier Specification for Improved Medical OOD Detection,
Uncertainty23(4591-4600)
IEEE DOI 2401
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Zhu, F.[Fei], Cheng, Z.[Zhen], Zhang, X.Y.[Xu-Yao], Liu, C.L.[Cheng-Lin],
OpenMix: Exploring Outlier Samples for Misclassification Detection,
CVPR23(12074-12083)
IEEE DOI 2309
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Madeira, P.[Pedro], Carreiro, A.[André], Gaudio, A.[Alex], Rosado, L.[Luís], Soares, F.[Filipe], Smailagic, A.[Asim],
ZEBRA: Explaining rare cases through outlying interpretable concepts,
XAI4CV23(3782-3788)
IEEE DOI 2309
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Wang, Z.J.[Zi-Jian], Luo, Y.[Yadan], Huang, Z.[Zi], Baktashmotlagh, M.[Mahsa],
FFM: Injecting Out-of-Domain Knowledge via Factorized Frequency Modification,
WACV23(4124-4133)
IEEE DOI 2302
Training, Image recognition, Perturbation methods, Frequency-domain analysis, Benchmark testing, and algorithms (including transfer) BibRef

Dua, R.[Radhika], Yang, S.[Seongjun], Li, Y.X.[Yi-Xuan], Choi, E.[Edward],
Task Agnostic and Post-hoc Unseen Distribution Detection,
WACV23(1350-1359)
IEEE DOI 2302
Training, Uncertainty, Aggregates, Medical services, Feature extraction, Natural language processing, Vision + language and/or other modalities BibRef

Borlino, F.C.[Francesco Cappio], Bucci, S.[Silvia], Tommasi, T.[Tatiana],
Semantic Novelty Detection via Relational Reasoning,
ECCV22(XXV:183-200).
Springer DOI 2211
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Hendrycks, D.[Dan], Zou, A.[Andy], Mazeika, M.[Mantas], Tang, L.[Leonard], Li, B.[Bo], Song, D.[Dawn], Steinhardt, J.[Jacob],
PixMix: Dreamlike Pictures Comprehensively Improve Safety Measures,
CVPR22(16762-16771)
IEEE DOI 2210
Safety critical applications. Training, Measurement, Uncertainty, Robustness, Fractals, Safety, Complexity theory, Representation learning, retrieval BibRef

Mao, C.Z.[Cheng-Zhi], Xia, K.[Kevin], Wang, J.[James], Wang, H.[Hao], Yang, J.F.[Jun-Feng], Bareinboim, E.[Elias], Vondrick, C.[Carl],
Causal Transportability for Visual Recognition,
CVPR22(7511-7521)
IEEE DOI 2210
Visualization, Correlation, Robustness, Classification algorithms, Object recognition, Representation learning BibRef

Hermann, M.[Matthias], Goldlücke, B.[Bastian], Franz, M.O.[Matthias O.],
Image Novelty Detection Based on Mean-Shift and Typical Set Size,
CIAP22(II:755-766).
Springer DOI 2205
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Germi, S.B.[Saeed Bakhshi], Rahtu, E.[Esa],
Enhanced Data-Recalibration: Utilizing Validation Data to Mitigate Instance-Dependent Noise in Classification,
CIAP22(I:621-632).
Springer DOI 2205
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Bai, Y.B.[Ying-Bin], Liu, T.L.[Tong-Liang],
Me-Momentum: Extracting Hard Confident Examples from Noisily Labeled Data,
ICCV21(9292-9301)
IEEE DOI 2203
Deep learning, Training, Shape, Neural networks, Training data, Benchmark testing, Recognition and classification BibRef

Wang, T.[Tan], Zhou, C.[Chang], Sun, Q.[Qianru], Zhang, H.W.[Han-Wang],
Causal Attention for Unbiased Visual Recognition,
ICCV21(3071-3080)
IEEE DOI 2203

WWW Link. Training, Location awareness, Visualization, Correlation, Annotations, Roads, Optimization and learning methods, Representation learning BibRef

Huang, J.K.[Jun-Kai], Fang, C.W.[Chao-Wei], Chen, W.K.[Wei-Kai], Chai, Z.H.[Zhen-Hua], Wei, X.L.[Xiao-Lin], Wei, P.X.[Peng-Xu], Lin, L.[Liang], Li, G.B.[Guan-Bin],
Trash to Treasure: Harvesting OOD Data with Cross-Modal Matching for Open-Set Semi-Supervised Learning,
ICCV21(8290-8299)
IEEE DOI 2203
Training, Representation learning, Matched filters, Semantics, Interference, Semisupervised learning, Filtering algorithms, Recognition and classification BibRef

Gorbett, M.[Matt], Blanchard, N.[Nathaniel],
Utilizing Network Features to Detect Erroneous Inputs,
VAQuality22(34-43)
IEEE DOI 2202
Support vector machines, Fault diagnosis, Data integrity, Computational modeling, Neural networks BibRef

Wei, X.Y.[Xin-Yue], Qiu, W.C.[Wei-Chao], Zhang, Y.[Yi], Xiao, Z.H.[Zi-Hao], Yuille, A.L.[Alan L.],
Nuisance-Label Supervision: Robustness Improvement by Free Labels,
ILDAV21(1541-1550)
IEEE DOI 2112
Image recognition, Annotations, Activity recognition, Feature extraction, Robustness BibRef

Ghosh, A.[Aritra], Lan, A.[Andrew],
Do We Really Need Gold Samples for Sample Weighting under Label Noise?,
WACV21(3921-3930)
IEEE DOI 2106
Training, Gold, Sensitivity, Neural networks, Benchmark testing BibRef

Cavalli, L.[Luca], Larsson, V.[Viktor], Oswald, M.R.[Martin Ralf], Sattler, T.[Torsten], Pollefeys, M.[Marc],
Handcrafted Outlier Detection Revisited,
ECCV20(XIX:770-787).
Springer DOI 2011
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You, C., Robinson, D.P., Vidal, R.,
Provable Self-Representation Based Outlier Detection in a Union of Subspaces,
CVPR17(4323-4332)
IEEE DOI 1711
Anomaly detection, Markov processes, Principal component analysis, Robustness, Sparse matrices, Tools BibRef

Piotto, N.[Nicola], Cordara, G.[Giovanni],
Statistical modelling for enhanced outlier detection,
ICIP14(4280-4284)
IEEE DOI 1502
Covariance matrices BibRef

Lee, K.H.[Kwang Hee], Lee, S.W.[Sang Wook],
Deterministic Fitting of Multiple Structures Using Iterative MaxFS with Inlier Scale Estimation,
ICCV13(41-48)
IEEE DOI 1403
MaxFS; fitting of multiple strucutres; inlier scale Robust fitting with outliers. BibRef

Goldstein, M.[Markus],
FastLOF: An Expectation-Maximization based Local Outlier detection algorithm,
ICPR12(2282-2285).
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Fritsch, V.[Virgile], Varoquaux, G.[Gaël], Poline, J.B.[Jean-Baptiste], Thirion, B.[Bertrand],
Non-parametric Density Modeling and Outlier-Detection in Medical Imaging Datasets,
MLMI12(210-217).
Springer DOI 1211
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Gao, Y.[Yan], Li, Y.Q.[Yi-Qun],
Improving Gaussian Process Classification with Outlier Detection, with Applications in Image Classification,
ACCV10(IV: 153-164).
Springer DOI 1011
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Tax, D.M.J.[David M. J.], Juszczak, P.[Piotr], Pekalska, E.[Elÿzbieta], Duin, R.P.W.[Robert P. W.],
Outlier Detection Using Ball Descriptions with Adjustable Metric,
SSPR06(587-595).
Springer DOI 0608
BibRef

Colliez, J., Dufrenois, F., Hamad, D.,
Robust Regression and Outlier Detection with SVR: Application to Optic Flow Estimation,
BMVC06(III:1229).
PDF File. 0609
BibRef

den Hollander, R.J.M., Hanjalic, A.,
Outlier identification in stereo correspondences using quadrics,
BMVC05(xx-yy).
HTML Version. 0509
Robust method for computing epipolar geometry from matches. BibRef

Park, J.H.[Jin-Hyeong], Zhang, Z.Y.[Zhen-Yue], Zha, H.Y.[Hong-Yuan], Kasturi, R.,
Local smoothing for manifold learning,
CVPR04(II: 452-459).
IEEE DOI 0408
Weighted smoothing for outlier detection. Build on weighted PCA. BibRef

Brailovsky, V.L.,
An Approach to Outlier Detection Based on Bayesian Probabilistic Model,
ICPR96(II: 70-74).
IEEE DOI 9608
(Tel-Aviv Univ., IL) BibRef

Chapter on Pattern Recognition, Clustering, Statistics, Grammars, Learning, Neural Nets, Genetic Algorithms continues in
Out of Distribution, OOD, Detection .