14.3.1 Outlier Detection and Analysis, Robust Analysis, Out of Distribution

Chapter Contents (Back)
Outliers. Out-of-Distribution. Robust Technique.

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

Urahama, K., Furukawa, Y.,
Gradient descent learning of nearest neighbor classifiers with outlier rejection,
PR(28), No. 5, May 1995, pp. 761-768.
Elsevier DOI 0401
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Black, M.J., Rangarajan, A.,
On The Unification of Line Processes, Outlier Rejection, and Robust Statistics with Applications in Early Vision,
IJCV(19), No. 1, July 1996, pp. 57-91.
Springer DOI
PDF File. 9608
BibRef
Earlier:
The Outlier Process: Unifying Line Processes and Robust Statistics,
CVPR94(15-22).
IEEE DOI Applied to reconstruction of degraded images. BibRef

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
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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
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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

Miller, D.J., Browning, J.,
A mixture model and EM-based algorithm for class discovery, robust classification, and outlier rejection in mixed labeled/unlabeled data sets,
PAMI(25), No. 11, November 2003, pp. 1468-1483.
IEEE Abstract. 0311
Augment the training set with unlabeled examples, assumed to come from a know class or a completely new class. Robust analysis. BibRef

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
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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

Grinstead, B.[Brad], Koschan, A.F.[Andreas F.], Gribok, A.V.[Andrei V.], Abidi, M.A.[Mongi A.], Gorsich, D.[David],
Outlier rejection by oriented tracks to aid pose estimation from video,
PRL(27), No. 1, 1 January 2006, pp. 37-48.
Elsevier DOI 0512
BibRef

Chang, H.[Hong], Yeung, D.Y.[Dit-Yan],
Robust locally linear embedding,
PR(39), No. 6, June 2006, pp. 1053-1065.
Elsevier DOI Nonlinear dimensionality reduction; Manifold learning; Locally linear embedding; Principal component analysis; Outlier; Robust statistics; M-estimation; Handwritten digit; Wood texture 0604
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

Lee, H.J.[Hyun-Jung], Seo, Y.D.[Yong-Duek], Lee, S.W.[Sang Wook],
Removing outliers by minimizing the sum of infeasibilities,
IVC(28), No. 6, June 2010, pp. 881-889.
Elsevier DOI 1003
The L-infinity optimization; Outlier removal; The sum of infeasibilities 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

Zhang, T., Huang, K., Li, X., Yang, J., Tao, D.,
Discriminative Orthogonal Neighborhood-Preserving Projections for Classification,
SMC-B(40), No. 1, February 2010, pp. 253-263.
IEEE DOI 0911
To overcome outlier problems in linear embedded classification. 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

Yu, S.X.[Stella X.],
Angular Embedding: A Robust Quadratic Criterion,
PAMI(34), No. 1, January 2012, pp. 158-173.
IEEE DOI 1112
given pairwise local ordering, find global ordering. Outlier removal. BibRef

Zhao, J.[Ji], Ma, J.[Jiayi], Tian, J.W.[Jin-Wen], Ma, J.[Jie], Zhang, D.[Dazhi],
A robust method for vector field learning with application to mismatch removing,
CVPR11(2977-2984).
IEEE DOI 1106
Vector Field Consensus (VFC). Distinguish inliers from outliers. 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

Ma, J.Y.[Jia-Yi], Jiang, X.Y.[Xing-Yu], Jiang, J.J.[Jun-Jun], Guo, X.J.[Xiao-Jie],
Robust Feature Matching Using Spatial Clustering With Heavy Outliers,
IP(29), No. 1, 2020, pp. 736-746.
IEEE DOI 1910
Task analysis, Clustering methods, Databases, Pattern matching, Complexity theory, mismatch removal 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

Antonello, N., Garner, P.N.,
A t-Distribution Based Operator for Enhancing Out of Distribution Robustness of Neural Network Classifiers,
SPLetters(27), 2020, pp. 1070-1074.
IEEE DOI 2007
Artificial neural networks, Gaussian distribution, Uncertainty, Training, Standards, Reliability, Neural networks, classification algorithms 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

Upadhyay, U., Mukherjee, P.,
Generating Out of Distribution Adversarial Attack Using Latent Space Poisoning,
SPLetters(28), 2021, pp. 523-527.
IEEE DOI 2103
Training, Aerospace electronics, Perturbation methods, Smoothing methods, Mathematical model, manifold space 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

Lin, C.[Chuang], Guo, S.X.[Shan-Xin], Chen, J.S.[Jin-Song], Sun, L.[Luyi], Zheng, X.R.[Xiao-Rou], Yang, Y.[Yan], Xiong, Y.F.[Ying-Fei],
Deep Learning Network Intensification for Preventing Noisy-Labeled Samples for Remote Sensing Classification,
RS(13), No. 9, 2021, pp. xx-yy.
DOI Link 2105
BibRef

Wang, P.[Peng], Niu, Y.X.[Yan-Xiong], Xiong, R.[Rui], Ma, F.[Fu], Zhang, C.X.[Chun-Xi],
DGANet: Dynamic Gradient Adjustment Anchor-Free Object Detection in Optical Remote Sensing Images,
RS(13), No. 9, 2021, pp. xx-yy.
DOI Link 2105
BibRef

Mensi, A.[Antonella], Bicego, M.[Manuele],
Enhanced anomaly scores for isolation forests,
PR(120), 2021, pp. 108115.
Elsevier DOI 2109
Anomaly detection, Isolation forest, Anomaly score, Outliers BibRef

Sáez, J.A.[José A.], Corchado, E.[Emilio],
ANCES: A novel method to repair attribute noise in classification problems,
PR(121), 2022, pp. 108198.
Elsevier DOI 2109
Correct attribute errors rather than remove samples. Attribute noise, Noise correction, Noise filtering, Noisy data, Classification BibRef

Ding, J.[Jiayu], Hu, X.[Xiao], Zhong, X.R.[Xiao-Rong],
A Semantic Encoding Out-of-Distribution Classifier for Generalized Zero-Shot Learning,
SPLetters(28), 2021, pp. 1395-1399.
IEEE DOI 2108
Semantics, Visualization, Encoding, Training, Task analysis, Manifolds, Benchmark testing, Generalized zero-shot learning, semantically consistent mapping BibRef

Wheeler, B.J.[Bradley J.], Karimi, H.A.[Hassan A.],
A semantically driven self-supervised algorithm for detecting anomalies in image sets,
CVIU(213), 2021, pp. 103279.
Elsevier DOI 2112
Anomaly detection, Self-supervised learning, Representation learning, Remote sensing, Multivariate statistics BibRef

Tai, M.[Mariko], Kudo, M.[Mineichi], Tanaka, A.[Akira], Imai, H.[Hideyuki], Kimura, K.[Keigo],
Kernelized Supervised Laplacian Eigenmap for Visualization and Classification of Multi-Label Data,
PR(123), 2022, pp. 108399.
Elsevier DOI 2112
Supervised Laplacian eigenmaps, Out-of-sample problem, Multi-label problems, Kernel trick, Separability-guided feature extraction 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

Zhou, H.[Haoyin], Jayender, J.[Jagadeesan],
EMDQ: Removal of Image Feature Mismatches in Real-Time,
IP(31), 2022, pp. 706-720.
IEEE DOI 2201
Strain, Feature extraction, Interpolation, Real-time systems, Impedance matching, Distortion, Feature matching, mismatch removal, deformation field BibRef

Sato, K.[Kazuki], Nakata, S.[Satoshi], Matsubara, T.[Takashi], Uehara, K.[Kuniaki],
Few-Shot Anomaly Detection Using Deep Generative Models for Grouped Data,
IEICE(E105-D), No. 2, February 2022, pp. 436-440.
WWW Link. 2202
BibRef

Ge, H.M.[Hai-Miao], Wang, L.[Liguo], 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

Sengupta, S.[Souhardya], Das, S.[Swagatam],
Selective Nearest Neighbors Clustering,
PRL(155), 2022, pp. 178-185.
Elsevier DOI 2203
Clustering, Nearest Neighbor, Border Detection, Border Peeling Clustering, Outlier detection BibRef

Zhang, M.[Minxue], Xu, N.[Ning], Geng, X.[Xin],
Feature-Induced Label Distribution for Learning with Noisy Labels,
PRL(155), 2022, pp. 107-113.
Elsevier DOI 2203
Label Noise, Label Distribution, Semi-supervised Learning, Deep Learning 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

Chen, S.X.[Shun-Xing], Zheng, L.X.[Lin-Xin], Xiao, G.B.[Guo-Bao], Zhong, Z.[Zhen], Ma, J.Y.[Jia-Yi],
CSDA-Net: Seeking reliable correspondences by channel-Spatial difference augment network,
PR(126), 2022, pp. 108539.
Elsevier DOI 2204
Feature matching, Deep learning, Outlier rejection, Attention mechanism BibRef

Wang, Z.P.[Zhi-Peng], Hou, C.P.[Chun-Ping], Ge, B.B.[Bang-Bang], Liu, Y.[Yang], Dong, Z.C.[Zhi-Cheng], Wu, Z.Q.[Zhi-Qiang],
Unsupervised anomaly detection via dual transformation-aware embeddings,
IET-IPR(16), No. 6, 2022, pp. 1657-1668.
DOI Link 2204
images that are globally or locally different from the training set. 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


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
BibRef

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
BibRef

Diers, J.[Jan], Pigorsch, C.[Christian],
Out-of-Distribution Detection Using Outlier Detection Methods,
CIAP22(III:15-26).
Springer DOI 2205
BibRef

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.[Junkai], Fang, C.[Chaowei], Chen, W.[Weikai], Chai, Z.H.[Zhen-Hua], Wei, X.L.[Xiao-Lin], Wei, P.X.[Peng-Xu], Lin, L.[Liang], Li, G.[Guanbin],
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

Chan, R.[Robin], Rottmann, M.[Matthias], Gottschalk, H.[Hanno],
Entropy Maximization and Meta Classification for Out-of-Distribution Detection in Semantic Segmentation,
ICCV21(5108-5117)
IEEE DOI 2203
Training, Measurement, Deep learning, Image segmentation, System performance, Semantics, Neural networks, Vision for robotics and autonomous vehicles BibRef

Yang, J.K.[Jing-Kang], Wang, H.Q.[Hao-Qi], Feng, L.T.[Li-Tong], Yan, X.P.[Xiao-Peng], Zheng, H.[Huabin], Zhang, W.[Wayne], Liu, Z.[Ziwei],
Semantically Coherent Out-of-Distribution Detection,
ICCV21(8281-8289)
IEEE DOI 2203
Degradation, Limiting, Soft sensors, Semantics, Pipelines, Dogs, Benchmark testing, Recognition and classification BibRef

Bai, H.Y.[Hao-Yue], Zhou, F.W.[Feng-Wei], Hong, L.Q.[Lan-Qing], Ye, N.Y.[Nan-Yang], Chan, S.-.H.G.[S.-H. Gary], Li, Z.G.[Zhen-Guo],
NAS-OoD: Neural Architecture Search for Out-of-Distribution Generalization,
ICCV21(8300-8309)
IEEE DOI 2203
Training, Industries, Error analysis, Training data, Computer architecture, Network architecture, Generators, Visual reasoning and logical representation BibRef

Hendrycks, D.[Dan], Basart, S.[Steven], Mu, N.[Norman], Kadavath, S.[Saurav], Wang, F.[Frank], Dorundo, E.[Evan], Desai, R.[Rahul], Zhu, T.[Tyler], Parajuli, S.[Samyak], Guo, M.[Mike], Song, D.[Dawn], Steinhardt, J.[Jacob], Gilmer, J.[Justin],
The Many Faces of Robustness: A Critical Analysis of Out-of-Distribution Generalization,
ICCV21(8320-8329)
IEEE DOI 2203
Degradation, Computational modeling, Benchmark testing, Gain measurement, Market research, Robustness, Recognition and classification BibRef

Besnier, V.[Victor], Bursuc, A.[Andrei], Picard, D.[David], Briot, A.[Alexandre],
Triggering Failures: Out-Of-Distribution detection by learning from local adversarial attacks in Semantic Segmentation,
ICCV21(15681-15690)
IEEE DOI 2203
Training, Image segmentation, Semantics, Training data, Focusing, Detectors, Vision for robotics and autonomous vehicles, grouping and shape BibRef

Tang, K.[Keke], Miao, D.[Dingruibo], Peng, W.L.[Wei-Long], Wu, J.[Jianpeng], Shi, Y.[Yawen], Gu, Z.[Zhaoquan], Tian, Z.H.[Zhi-Hong], Wang, W.P.[Wen-Ping],
CODEs: Chamfer Out-of-Distribution Examples against Overconfidence Issue,
ICCV21(1133-1142)
IEEE DOI 2203
Training, Deep learning, Codes, Neural networks, Training data, Generative adversarial networks, Explainable AI, 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, Conferences, Computational modeling, Neural networks BibRef

Albert, P.[Paul], Ortego, D.[Diego], Arazo, E.[Eric], O'Connor, N.E.[Noel E.], McGuinness, K.[Kevin],
Addressing out-of-distribution label noise in webly-labelled data,
WACV22(2393-2402)
IEEE DOI 2202
Training, Visualization, Heuristic algorithms, Buildings, Neural networks, Search engines, Noise robustness, Deep Learning Image classification on web crawled datasets BibRef

Vendramini, M.[Marcos], Oliveira, H.[Hugo], Machado, A.[Alexei], dos Santos, J.A.[Jefersson A.],
Opening Deep Neural Networks With Generative Models,
ICIP21(1314-1318)
IEEE DOI 2201
Deep learning, Training, Visualization, Protocols, Neural networks, Feature extraction, Convolutional neural networks, Out-of-Distribution Detection BibRef

Wei, X.Y.[Xin-Yue], Qiu, W.C.[Wei-Chao], Zhang, Y.[Yi], Xiao, Z.[Zihao], 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

Zaeemzadeh, A.[Alireza], Bisagno, N.[Niccolò], Sambugaro, Z.[Zeno], Conci, N.[Nicola], Rahnavard, N.[Nazanin], Shah, M.[Mubarak],
Out-of-Distribution Detection Using Union of 1-Dimensional Subspaces,
CVPR21(9447-9456)
IEEE DOI 2111
Deep learning, Training, Measurement, Training data, Benchmark testing, Feature extraction BibRef

Lin, Z.Q.[Zi-Qian], Roy, S.D.[Sreya Dutta], Li, Y.X.[Yi-Xuan],
MOOD: Multi-level Out-of-distribution Detection,
CVPR21(15308-15318)
IEEE DOI 2111
Mood, Complexity theory, Pattern recognition, Computational efficiency BibRef

Huang, R.[Rui], Li, Y.X.[Yi-Xuan],
MOS: Towards Scaling Out-of-distribution Detection for Large Semantic Space,
CVPR21(8706-8715)
IEEE DOI 2111
Bridges, Image resolution, Semantics, Machine learning, Benchmark testing, Pattern recognition BibRef

Zhang, X.X.[Xing-Xuan], Cui, P.[Peng], Xu, R.Z.[Ren-Zhe], Zhou, L.J.[Lin-Jun], He, Y.[Yue], Shen, Z.[Zheyan],
Deep Stable Learning for Out-Of-Distribution Generalization,
CVPR21(5368-5378)
IEEE DOI 2111
Training, Deep learning, Correlation, Computational modeling, Training data, Benchmark testing 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

Begon, J.M.[Jean-Michel], Geurts, P.[Pierre],
Sample-free white-box out-of-distribution detection for deep learning,
TCV21(3285-3294)
IEEE DOI 2109
Deep learning, Filtering, Computational modeling, Computer architecture, Data models BibRef

Möller, F.[Felix], Botache, D.[Diego], Huseljic, D.[Denis], Heidecker, F.[Florian], Bieshaar, M.[Maarten], Sick, B.[Bernhard],
Out-of-distribution Detection and Generation using Soft Brownian Offset Sampling and Autoencoders,
SAIAD21(46-55)
IEEE DOI 2109
Training, Deep learning, Time series analysis, Neural networks, Transforms, Prediction algorithms, Trajectory BibRef

Marson, L.[Luca], Li, V.[Vladimir], Maki, A.[Atsuto],
Boundary Optimised Samples Training for Detecting Out-of-Distribution Images,
ICPR21(10486-10492)
IEEE DOI 2105
Training, Measurement, Toy manufacturing industry, Training data, Data visualization, Benchmark testing, Propagation losses BibRef

Sharma, K.[Karishma], Donmez, P.[Pinar], Luo, E.[Enming], Liu, Y.[Yan], Yalniz, I.Z.[I. Zeki],
Noiserank: Unsupervised Label Noise Reduction with Dependence Models,
ECCV20(XXVII:737-753).
Springer DOI 2011
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Chen, X.Y.[Xing-Yu], Lan, X.G.[Xu-Guang], Sun, F.C.[Fu-Chun], Zheng, N.N.[Nan-Ning],
A Boundary Based Out-of-Distribution Classifier for Generalized Zero-shot Learning,
ECCV20(XXIV:572-588).
Springer DOI 2012
BibRef

Zisselman, E.[Ev], Tamar, A.[Aviv],
Deep Residual Flow for Out of Distribution Detection,
CVPR20(13991-14000)
IEEE DOI 2008
detecting out-of-distribution examples. Gaussian distribution, Neural networks, Data models, Training, Jacobian matrices, Computer architecture, Maximum likelihood detection BibRef

Mundt, M., Pliushch, I., Majumder, S., Ramesh, V.,
Open Set Recognition Through Deep Neural Network Uncertainty: Does Out-of-Distribution Detection Require Generative Classifiers?,
SDL-CV19(753-757)
IEEE DOI 2004
Bayes methods, image classification, neural nets, object detection, statistical analysis, statistical distributions, out of distribution detection BibRef

Yu, Q., Aizawa, K.,
Unknown Class Label Cleaning For Learning With Open-Set Noisy Labels,
ICIP20(1731-1735)
IEEE DOI 2011
Noise measurement, Training, Training data, Cleaning, Optimization, Neural networks, Robustness, Noisy label, label cleaning, open-set image classification 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
BibRef

Kwon, G.[Gukyeong], Prabhushankar, M.[Mohit], Temel, D.[Dogancan], AlRegib, G.[Ghassan],
Backpropagated Gradient Representations for Anomaly Detection,
ECCV20(XXI:206-226).
Springer DOI 2011
BibRef

Techapanurak, E.[Engkarat], Suganuma, M.[Masanori], Okatani, T.[Takayuki],
Hyperparameter-free Out-of-distribution Detection Using Cosine Similarity,
ACCV20(IV:53-69).
Springer DOI 2103
BibRef

Hsu, Y., Shen, Y., Jin, H., Kira, Z.,
Generalized ODIN: Detecting Out-of-Distribution Image Without Learning From Out-of-Distribution Data,
CVPR20(10948-10957)
IEEE DOI 2008
Neural networks, Semantics, Training, Tuning, Data preprocessing, Predictive models, Pins BibRef

Kwon, G.[Gukyeong], Prabhushankar, M.[Mohit], Temel, D.[Dogancan], Al Regib, G.[Ghassan],
Distorted Representation Space Characterization Through Backpropagated Gradients,
ICIP19(2651-2655)
IEEE DOI 1910
Gradients, Representation Learning, Out-of-distribution, Image Quality Assessment, Autoencoder BibRef

Yu, Q.[Qing], Aizawa, K.[Kiyoharu],
Unsupervised Out-of-Distribution Detection by Maximum Classifier Discrepancy,
ICCV19(9517-9525)
IEEE DOI 2004
Using 2 networks -- when is input reasonable. convolutional neural nets, feature extraction, learning (artificial intelligence), pattern classification BibRef

Vyas, A.[Apoorv], Jammalamadaka, N.[Nataraj], Zhu, X.[Xia], Das, D.[Dipankar], Kaul, B.[Bharat], Willke, T.L.[Theodore L.],
Out-of-Distribution Detection Using an Ensemble of Self Supervised Leave-Out Classifiers,
ECCV18(VIII: 560-574).
Springer DOI 1810
BibRef

Wu, X.[Xin], Cai, L.[Ling], Ji, R.R.[Rong-Rong],
Gamma Mixture Models for Outlier Removal,
ICIP18(828-832)
IEEE DOI 1809
Outlier in training samples. Training, Boosting, Probability, Mixture models, Probabilistic logic, Task analysis, Gamma Mixture Model, Outlier Removal, Deep Neural Network BibRef

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

Liu, W.[Wei], Hua, G.[Gang], Smith, J.R.[John R.],
Unsupervised One-Class Learning for Automatic Outlier Removal,
CVPR14(3826-3833)
IEEE DOI 1409
One-Class Learning; Outlier Removal 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).
WWW Link. 1302
BibRef

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
BibRef

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
BibRef

Seo, Y.D.[Yong-Duek], Lee, H.J.[Hyun-Jung], Lee, S.W.[Sang Wook],
Outlier Removal by Convex Optimization for L-Infinity Approaches,
PSIVT09(203-214).
Springer DOI 0901
BibRef

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

Sim, K.[Kristy], Hartley, R.I.[Richard I.],
Removing Outliers Using The L-inf Norm,
CVPR06(I: 485-494).
IEEE DOI 0606

See also Recovering Camera Motion Using L-inf Minimization. BibRef

Hautamäki, V.[Ville], Cherednichenko, S.[Svetlana], Kärkkäinen, I.[Ismo], Kinnunen, T.[Tomi], Fränti, P.[Pasi],
Improving K-Means by Outlier Removal,
SCIA05(978-987).
Springer DOI 0506
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
Boosting, AdaBoost Technique .


Last update:Aug 14, 2022 at 21:20:19