14.3.2 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,
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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,
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Springer DOI
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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,
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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,
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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
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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
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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
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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

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

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.Y.[Jia-Yi], Tian, J.W.[Jin-Wen], Ma, J.[Jie], Zhang, D.Z.[Da-Zhi],
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

Kudo, M.[Mineichi], Kimura, K.[Keigo], Morishita, S.[Shumpei], Sun, L.[Lu],
Efficient Leave-One-Out Evaluation of Kernelized Implicit Mappings,
SSSPR22(223-232).
Springer DOI 2301
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.Y.[Hao-Yin], 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.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

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

Wang, L.[Lei], Huang, S.[Sheng], Huangfu, L.[Luwen], Liu, B.[Bo], Zhang, X.H.[Xiao-Hong],
Multi-label out-of-distribution detection via exploiting sparsity and co-occurrence of labels,
IVC(126), 2022, pp. 104548.
Elsevier DOI 2209
Multi-label learning, Out-of-distribution detection, Image classification, Sparse learning, Label co-occurrence 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

Li, Y.[Yanfu], Yang, X.P.[Xiao-Peng], Chen, L.[Liang], Zhi, Y.J.[Ying-Jian], Liu, H.L.[Hong-Li],
Robust Registration of Rail Profile and Complete Detection of Outliers in Complex Field Environment,
ITS(23), No. 11, November 2022, pp. 20098-20109.
IEEE DOI 2212
Rails, Standards, Pollution measurement, Inspection, Measurement by laser beam, Data models, Manuals, R-H-ICP BibRef

Shi, Z.W.[Zi-Wei], Xiao, G.B.[Guo-Bao], Zheng, L.X.[Lin-Xin], Ma, J.Y.[Jia-Yi], Chen, R.Q.[Ri-Qing],
JRA-Net: Joint representation attention network for correspondence learning,
PR(135), 2023, pp. 109180.
Elsevier DOI 2212
Correspondences, Joint representation, Attention mechanism, Outlier rejection, Pose estimation 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

Dagaev, N.[Nikolay], Roads, B.D.[Brett D.], Luo, X.L.[Xiao-Liang], Barry, D.N.[Daniel N.], Patil, K.R.[Kaustubh R.], Love, B.C.[Bradley C.],
A too-good-to-be-true prior to reduce shortcut reliance,
PRL(166), 2023, pp. 164-171.
Elsevier DOI 2302
Shortcut learning, Out-of-distribution generalization, Robustness, Deep learning BibRef

Calli, E.[Erdi], van Ginneken, B.[Bram], Sogancioglu, E.[Ecem], Murphy, K.[Keelin],
FRODO: An In-Depth Analysis of a System to Reject Outlier Samples From a Trained Neural Network,
MedImg(42), No. 4, April 2023, pp. 971-981.
IEEE DOI 2304
Task analysis, Biomedical imaging, X-ray imaging, Measurement, Training, Neural networks, Deep learning, Deep learning, statistics 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

Chen, Z.[Zhi], Duan, J.[Jiang], Kang, L.[Li], Qiu, G.P.[Guo-Ping],
Supervised Anomaly Detection via Conditional Generative Adversarial Network and Ensemble Active Learning,
PAMI(45), No. 6, June 2023, pp. 7781-7798.
IEEE DOI 2305
Detectors, Anomaly detection, Generative adversarial networks, Ensemble learning, Training, Generators, Task analysis, outlier detection BibRef

Arias, L.A.S.[Luis Antonio Souto], Oosterlee, C.W.[Cornelis W.], Cirillo, P.[Pasquale],
AIDA: Analytic isolation and distance-based anomaly detection algorithm,
PR(141), 2023, pp. 109607.
Elsevier DOI 2306
Outlier detection, Anomaly explanation, Isolation, Distance, Ensemble methods BibRef

Wei, S.X.[Shen-Xing], Wei, X.[Xing], Kurniawan, M.R.[Muhammad Rifki], Ma, Z.H.[Zhi-Heng], Gong, Y.H.[Yi-Hong],
Topology-preserving transfer learning for weakly-supervised anomaly detection and segmentation,
PRL(170), 2023, pp. 77-84.
Elsevier DOI 2306
Anomaly detection, Transfer learning, Weakly-supervised learning, Topology preservation 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

Kirgo, M.[Maxime], Terrasse, G.[Guillaume], Thibault, G.[Guillaume], Ovsjanikov, M.[Maks],
ReVISOR: ResUNets with visibility and intensity for structured outlier removal,
PandRS(202), 2023, pp. 184-204.
Elsevier DOI 2308
Outlier detection, Deep learning, Point cloud semantic segmentation BibRef

Yang, J.K.[Jing-Kang], Zhou, K.Y.[Kai-Yang], Liu, Z.W.[Zi-Wei],
Full-Spectrum Out-of-Distribution Detection,
IJCV(131), No. 10, October 2023, pp. 2607-2622.
Springer DOI 2309
BibRef

Kumano, S.[Soichiro], Kera, H.[Hiroshi], Yamasaki, T.[Toshihiko],
Sparse fooling images: Fooling machine perception through unrecognizable images,
PRL(172), 2023, pp. 259-265.
Elsevier DOI 2309
Fooling images, Out-of-distribution, Vulnerability of classifier 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

Zhang, J.[Ji], Gao, L.L.[Lian-Li], Hao, B.G.[Bing-Guang], Huang, H.[Hao], Song, J.K.[Jing-Kuan], Shen, H.T.[Heng-Tao],
From Global to Local: Multi-Scale Out-of-Distribution Detection,
IP(32), 2023, pp. 6115-6128.
IEEE DOI Code:
WWW Link. 2311
BibRef

Zhao, Z.L.[Zhi-Lin], Cao, L.[Longbing], Lin, K.Y.[Kun-Yu],
Supervision Adaptation Balancing In-Distribution Generalization and Out-of-Distribution Detection,
PAMI(45), No. 12, December 2023, pp. 15743-15758.
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

Chen, Z.[Zhe], Ding, Z.Q.[Zhi-Quan], Zhang, X.L.[Xiao-Ling], Zhang, X.[Xin], Qin, T.Q.[Tian-Qi],
Improving Out-of-Distribution Generalization in SAR Image Scene Classification with Limited Training Samples,
RS(15), No. 24, 2023, pp. 5761.
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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
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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

Fayyad, J.[Jamil], Gupta, K.[Kashish], Mahdian, N.[Navid], Gruyer, D.[Dominique], Najjaran, H.[Homayoun],
Exploiting classifier inter-level features for efficient out-of-distribution detection,
IVC(142), 2024, pp. 104897.
Elsevier DOI 2402
Out-of-distribution detection, Deep learning-based classification, Machine learning, Intermediate feature extraction BibRef

Lehner, A.[Alexander], Gasperini, S.[Stefano], Marcos-Ramiro, A.[Alvaro], Schmidt, M.[Michael], Navab, N.[Nassir], Busam, B.[Benjamin], Tombari, F.[Federico],
3D Adversarial Augmentations for Robust Out-of-Domain Predictions,
IJCV(132), No. 3, March 2024, pp. 931-963.
Springer DOI 2402
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Yu, Y.[Yeonguk], Shin, S.[Sungho], Ko, M.W.[Minh-Wan], Lee, K.[Kyoobin],
Exploring using jigsaw puzzles for out-of-distribution detection,
CVIU(241), 2024, pp. 103968.
Elsevier DOI Code:
WWW Link. 2403
Neural networks, Image recognition, Out-of-distribution detection BibRef

Wang, Y.J.[Yu-Jie], Yu, K.[Kui], Xiang, G.[Guodu], Cao, F.Y.[Fu-Yuan], Liang, J.[Jiye],
Discovering causally invariant features for out-of-distribution generalization,
PR(150), 2024, pp. 110338.
Elsevier DOI 2403
Out-of-distribution generalization, Local causal structure learning, Causal effect estimation BibRef

Wu, A.[Aming], Deng, C.[Cheng], Liu, W.[Wei],
Unsupervised Out-of-Distribution Object Detection via PCA-Driven Dynamic Prototype Enhancement,
IP(33), 2024, pp. 2431-2446.
IEEE DOI 2404
Feature extraction, Prototypes, Object detection, Detectors, Semantics, Principal component analysis, Training, discrimination 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

Maškov¡, M.[Michaela], Zorek, M.[Matej], Pevny, T.[Tom¡š], Šmidl, V.[V¡clav],
Deep anomaly detection on set data: Survey and comparison,
PR(151), 2024, pp. 110381.
Elsevier DOI 2404
Set data, Anomaly detection, Generative models, One-class classification, Set transformers BibRef


Krumpl, G.[Gerhard], Avenhaus, H.[Henning], Possegger, H.[Horst], Bischof, H.[Horst],
ATS: Adaptive Temperature Scaling for Enhancing Out-of-Distribution Detection Methods,
WACV24(3852-3861)
IEEE DOI 2404
Training, Adaptation models, Computational modeling, Machine learning, Benchmark testing, Feature extraction, Image recognition and understanding BibRef

Kirchheim, K.[Konstantin], Gonschorek, T.[Tim], Ortmeier, F.[Frank],
Out-of-Distribution Detection with Logical Reasoning,
WACV24(2111-2120)
IEEE DOI 2404
Training, Codes, Knowledge based systems, Knowledge representation, Machine learning, Cognition, Algorithms, Image recognition and understanding BibRef

Qiu, X.[Xinkuan], Kan, M.[Meina], Zhou, Y.B.[Yong-Bin], Bi, Y.C.[Yan-Chao], Shan, S.G.[Shi-Guang],
Shape-biased CNNs are Not Always Superior in Out-of-Distribution Robustness,
WACV24(2315-2324)
IEEE DOI 2404
Deep learning, Adaptation models, Head, Shape, Design methodology, Stars, Algorithms, Machine learning architectures, formulations, and algorithms BibRef

Mehta, N.[Nikhil], Liang, K.J.[Kevin J], Huang, J.[Jing], Chu, F.J.[Fu-Jen], Yin, L.[Li], Hassner, T.[Tal],
HyperMix: Out-of-Distribution Detection and Classification in Few-Shot Settings,
WACV24(2399-2409)
IEEE DOI 2404
Computational modeling, Machine learning, Data models, Task analysis, Standards, Algorithms 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
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Jeon, M.[Myeongho], Kang, M.[Myungjoo], Lee, J.[Joonseok],
A Unified Framework for Robustness on Diverse Sampling Errors,
ICCV23(1464-1472)
IEEE DOI 2401
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Noh, S.[SoonCheol], Jeong, D.[DongEon], Lee, J.H.[Jee-Hyong],
Simple and Effective Out-of-Distribution Detection via Cosine-based Softmax Loss,
ICCV23(16514-16523)
IEEE DOI 2401
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Zou, Y.[Yuli], Deng, W.J.[Wei-Jian], Zheng, L.[Liang],
Adaptive Calibrator Ensemble: Navigating Test Set Difficulty in Out-of-Distribution Scenarios,
ICCV23(19276-19285)
IEEE DOI Code:
WWW Link. 2401
BibRef

Guan, X.Y.[Xiao-Yuan], Liu, Z.[Zhouwu], Zheng, W.S.[Wei-Shi], Zhou, Y.[Yuren], Wang, R.X.[Rui-Xuan],
Revisit PCA-based technique for Out-of-Distribution Detection,
ICCV23(19374-19382)
IEEE DOI Code:
WWW Link. 2401
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Li, J.L.[Jing-Lun], Zhou, X.Y.[Xin-Yu], Guo, P.[Pinxue], Sun, Y.X.[Yi-Xuan], Huang, Y.[Yiwen], Ge, W.F.[Wei-Feng], Zhang, W.Q.[Wen-Qiang],
Hierarchical Visual Categories Modeling: A Joint Representation Learning and Density Estimation Framework for Out-of-Distribution Detection,
ICCV23(23368-23378)
IEEE DOI 2401
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Wilson, S.[Samuel], Fischer, T.[Tobias], Dayoub, F.[Feras], Miller, D.[Dimity], Sünderhauf, N.[Niko],
SAFE: Sensitivity-Aware Features for Out-of-Distribution Object Detection,
ICCV23(23508-23519)
IEEE DOI 2401
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Aguilar, E.[Eduardo], Raducanu, B.[Bogdan], Radeva, P.[Petia], van de Weijer, J.[Joost],
Continual Evidential Deep Learning for Out-of-Distribution Detection,
VCL23(3436-3446)
IEEE DOI 2401
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Ojaswee, Agarwal, A.[Akshay], Ratha, N.[Nalini],
Benchmarking Image Classifiers for Physical Out-of-Distribution Examples Detection,
OutDistri23(4429-4437)
IEEE DOI 2401
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Cultrera, L.[Luca], Seidenari, L.[Lorenzo], del Bimbo, A.[Alberto],
Leveraging Visual Attention for out-of-distribution Detection,
OutDistri23(4449-4458)
IEEE DOI 2401
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Galesso, S.[Silvio], Argus, M.[Max], Brox, T.[Thomas],
Far Away in the Deep Space: Dense Nearest-Neighbor-Based Out-of-Distribution Detection,
Uncertainty23(4479-4489)
IEEE DOI 2401
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Hammam, A.[Ahmed], Bonarens, F.[Frank], Ghobadi, S.E.[Seyed Eghabl], Stiller, C.[Christoph],
Identifying Out-of-Domain Objects with Dirichlet Deep Neural Networks,
Uncertainty23(4562-4571)
IEEE DOI 2401
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Averly, R.[Reza], Chao, W.L.[Wei-Lun],
Unified Out-Of-Distribution Detection: A Model-Specific Perspective,
ICCV23(1453-1463)
IEEE DOI 2401
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Park, J.[Jaewoo], Chai, J.C.L.[Jacky Chen Long], Yoon, J.[Jaeho], Teoh, A.B.J.[Andrew Beng Jin],
Understanding the Feature Norm for Out-of-Distribution Detection,
ICCV23(1557-1567)
IEEE DOI 2401
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Park, J.[Jaewoo], Jung, Y.G.[Yoon Gyo], Teoh, A.B.J.[Andrew Beng Jin],
Nearest Neighbor Guidance for Out-of-Distribution Detection,
ICCV23(1686-1695)
IEEE DOI 2401
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Mukhoti, J.[Jishnu], Lin, T.Y.[Tsung-Yu], Chen, B.C.[Bor-Chun], Shah, A.[Ashish], Torr, P.H.S.[Philip H.S.], Dokania, P.K.[Puneet K.], Lim, S.N.[Ser-Nam],
Raising the Bar on the Evaluation of Out-of-Distribution Detection,
OutDistri23(4367-4377)
IEEE DOI 2401
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Tomáš, V.[Vojí?r], Šochman, J.[Jan], Aljundi, R.[Rahaf], Matas, J.[Jirí],
Calibrated Out-of-Distribution Detection with a Generic Representation,
Uncertainty23(4509-4518)
IEEE DOI Code:
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Martins, N.P.[Nuno Pimpão], Kalaidzidis, Y.[Yannis], Zerial, M.[Marino], Jug, F.[Florian],
DeepContrast: Deep Tissue Contrast Enhancement using Synthetic Data Degradations and OOD Model Predictions,
BioIm23(3830-3839)
IEEE DOI 2401
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Wu, A.[Aming], Chen, D.[Da], Deng, C.[Cheng],
Deep Feature Deblurring Diffusion for Detecting Out-of-Distribution Objects,
ICCV23(13335-13345)
IEEE DOI Code:
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Zhang, M.[Min], Yuan, J.[Junkun], He, Y.[Yue], Li, W.B.[Wen-Bin], Chen, Z.Y.[Zheng-Yu], Kuang, K.[Kun],
MAP: Towards Balanced Generalization of IID and OOD through Model-Agnostic Adapters,
ICCV23(11887-11897)
IEEE DOI 2401
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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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Chen, Y.[Yiye], Lin, Y.Z.[Yun-Zhi], Xu, R.N.[Rui-Nian], Vela, P.A.[Patricio A.],
WDiscOOD: Out-of-Distribution Detection via Whitened Linear Discriminant Analysis,
ICCV23(5275-5284)
IEEE DOI 2401
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Lu, L.L.[Lorenzo Li], d'Ascenzi, G.[Giulia], Borlino, F.C.[Francesco Cappio], Tommasi, T.[Tatiana],
Large Class Separation is Not What You Need for Relational Reasoning-Based OOD Detection,
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Springer DOI 2312
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Wu, Y.F.[Yi-Fan], Dai, S.M.[Song-Min], Pan, D.Y.[Deng-Ye], Li, X.Q.[Xiao-Qiang],
OEST: Outlier Exposure by Simple Transformations for Out-of-Distribution Detection,
ICIP23(2170-2174)
IEEE DOI 2312
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Tang, K.[Keke], Cai, X.J.[Xu-Jian], Peng, W.L.[Wei-Long], Li, S.D.[Shu-Dong], Wang, W.P.[Wen-Ping],
OOD Attack: Generating Overconfident out-of-Distribution Examples to Fool Deep Neural Classifiers,
ICIP23(1260-1264)
IEEE DOI 2312
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Kang, D.[Dohee], Kang, S.[Somang], Kim, D.[Daeha], Song, B.C.[Byung Cheol],
Modality-Aware OOD Suppression Using Feature Discrepancy for Multi-Modal Emotion Recognition,
ICIP23(1035-1039)
IEEE DOI 2312
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Yu, J.C.[Jun-Chi], Liang, J.[Jian], He, R.[Ran],
Mind the Label Shift of Augmentation-based Graph OOD Generalization,
CVPR23(11620-11630)
IEEE DOI 2309
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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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Li, T.[Tang], Qiao, F.C.[Feng-Chun], Ma, M.M.[Meng-Meng], Peng, X.[Xi],
Are Data-Driven Explanations Robust Against Out-of-Distribution Data?,
CVPR23(3821-3831)
IEEE DOI 2309
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Yu, R.[Runpeng], Liu, S.[Songhua], Yang, X.Y.[Xing-Yi], Wang, X.C.[Xin-Chao],
Distribution Shift Inversion for Out-of-Distribution Prediction,
CVPR23(3592-3602)
IEEE DOI 2309
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Zhang, Z.H.[Zi-Han], Xiang, X.[Xiang],
Decoupling MaxLogit for Out-of-Distribution Detection,
CVPR23(3388-3397)
IEEE DOI 2309
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Olber, B.[Bartlomiej], Radlak, K.[Krystian], Popowicz, A.[Adam], Szczepankiewicz, M.[Michal], Chachula, K.[Krystian],
Detection of Out-of-Distribution Samples Using Binary Neuron Activation Patterns,
CVPR23(3378-3387)
IEEE DOI 2309
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Lu, F.[Fan], Zhu, K.[Kai], Zhai, W.[Wei], Zheng, K.[Kecheng], Cao, Y.[Yang],
Uncertainty-Aware Optimal Transport for Semantically Coherent Out-of-Distribution Detection,
CVPR23(3282-3291)
IEEE DOI 2309
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Liu, Q.H.[Qi-Hao], Kortylewski, A.[Adam], Yuille, A.[Alan],
PoseExaminer: Automated Testing of Out-of-Distribution Robustness in Human Pose and Shape Estimation,
CVPR23(672-681)
IEEE DOI 2309
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Choi, H.[Hyunjun], Jeong, H.[Hawook], Choi, J.Y.[Jin Young],
Balanced Energy Regularization Loss for Out-of-distribution Detection,
CVPR23(15691-15700)
IEEE DOI 2309
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Yu, Y.[Yeonguk], Shin, S.[Sungho], Lee, S.[Seongju], Jun, C.H.[Chang-Hyun], Lee, K.[Kyoobin],
Block Selection Method for Using Feature Norm in Out-of-Distribution Detection,
CVPR23(15701-15711)
IEEE DOI 2309
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Ahn, Y.H.[Yong Hyun], Park, G.M.[Gyeong-Moon], Kim, S.T.[Seong Tae],
LINe: Out-of-Distribution Detection by Leveraging Important Neurons,
CVPR23(19852-19862)
IEEE DOI 2309
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Wang, Y.[Yu], Qiao, P.C.[Peng-Chong], Liu, C.[Chang], Song, G.[Guoli], Zheng, X.[Xiawu], Chen, J.[Jie],
Out-of-Distributed Semantic Pruning for Robust Semi-Supervised Learning,
CVPR23(23849-23858)
IEEE DOI 2309
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Liu, X.X.[Xi-Xi], Lochman, Y.[Yaroslava], Zach, C.[Christopher],
GEN: Pushing the Limits of Softmax-Based Out-of-Distribution Detection,
CVPR23(23946-23955)
IEEE DOI 2309
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Chali, S.[Samy], Kucher, I.[Inna], Duranton, M.[Marc], Klein, J.O.[Jacques-Olivier],
Improving Normalizing Flows with the Approximate Mass for Out-of-Distribution Detection,
GCV23(750-758)
IEEE DOI 2309
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Graham, M.S.[Mark S.], Pinaya, W.H.L.[Walter H. L.], Tudosiu, P.D.[Petru-Daniel], Nachev, P.[Parashkev], Ourselin, S.[Sebastien], Cardoso, M.J.[M. Jorge],
Denoising diffusion models for out-of-distribution detection,
VAND23(2948-2957)
IEEE DOI 2309
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Humblot-Renaux, G.[Galadrielle], Escalera, S.[Sergio], Moeslund, T.B.[Thomas B.],
Beyond AUROC and co. for evaluating out-of-distribution detection performance,
SAIAD23(3881-3890)
IEEE DOI 2309
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Xia, G.X.[Guo-Xuan], Bouganis, C.S.[Christos-Savvas],
Augmenting Softmax Information for Selective Classification with Out-of-distribution Data,
ACCV22(VI:664-680).
Springer DOI 2307
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Maag, K.[Kira], Chan, R.[Robin], Uhlemeyer, S.[Svenja], Kowol, K.[Kamil], Gottschalk, H.[Hanno],
Two Video Data Sets for Tracking and Retrieval of Out of Distribution Objects,
ACCV22(V:476-494).
Springer DOI 2307
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Cohen, N.[Niv], Abutbul, R.[Ron], Hoshen, Y.[Yedid],
Out-of-distribution Detection Without Class Labels,
LLID22(101-117).
Springer DOI 2304
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Galesso, S.[Silvio], Bravo, M.A.[Maria Alejandra], Naouar, M.[Mehdi], Brox, T.[Thomas],
Probing Contextual Diversity for Dense Out-of-Distribution Detection,
SafeDrive22(492-509).
Springer DOI 2304
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Zhang, X.X.[Xing-Xuan], He, Y.[Yue], Wang, T.[Tan], Qi, J.X.[Jia-Xin], Yu, H.[Han], Wang, Z.[Zimu], Peng, J.[Jie], Xu, R.Z.[Ren-Zhe], Shen, Z.[Zheyan], Niu, Y.[Yulei], Zhang, H.W.[Han-Wang], Cui, P.[Peng],
Nico Challenge: Out-of-distribution Generalization for Image Recognition Challenges,
CiV22(433-450).
Springer DOI 2304
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Liu, H.Z.[Hao-Zhe], Zhang, W.[Wentian], Xie, J.[Jinheng], Wu, H.Q.[Hao-Qian], Li, B.[Bing], Zhang, Z.Q.[Zi-Qi], Li, Y.X.[Yue-Xiang], Huang, Y.W.[Ya-Wen], Ghanem, B.[Bernard], Zheng, Y.F.[Ye-Feng],
Decoupled Mixup for Out-of-distribution Visual Recognition,
CiV22(451-464).
Springer DOI 2304
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Chen, Z.N.[Zi-Ning], Wang, W.Q.[Wei-Qiu], Zhao, Z.C.[Zhi-Cheng], Men, A.[Aidong], Chen, H.[Hong],
Bag of Tricks for Out-of-distribution Generalization,
CiV22(465-476).
Springer DOI 2304
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Wang, J.H.[Jia-Hao], Wang, H.[Hao], Dong, Z.[Zhuojun], Yang, H.[Hua], Yang, Y.T.[Yu-Ting], Bao, Q.[Qianyue], Liu, F.[Fang], Jiao, L.C.[Li-Cheng],
A Three-stage Model Fusion Method for Out-of-distribution Generalization,
CiV22(488-499).
Springer DOI 2304
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Wang, Y.Q.[Yu-Qing], Li, X.X.[Xiang-Xian], Qi, Z.[Zhuang], Li, J.Y.[Jing-Yu], Li, X.L.[Xue-Long], Meng, X.X.[Xiang-Xu], Meng, L.[Lei],
Meta-causal Feature Learning for Out-of-distribution Generalization,
CiV22(530-545).
Springer DOI 2304
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Yousef, M.[Mohamed], Ackermann, M.[Marcel], Kurup, U.[Unmesh], Bishop, T.[Tom],
No Shifted Augmentations (NSA): Compact distributions for robust self-supervised Anomaly Detection,
WACV23(5500-5509)
IEEE DOI 2302
Representation learning, Measurement, Pollution, Costs, Source coding, Training data, Feature extraction, visual reasoning BibRef

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

Zhang, J.Y.[Jing-Yang], Inkawhich, N.[Nathan], Linderman, R.[Randolph], Chen, Y.[Yiran], Li, H.[Hai],
Mixture Outlier Exposure: Towards Out-of-Distribution Detection in Fine-grained Environments,
WACV23(5520-5529)
IEEE DOI 2302
Training, Image recognition, Codes, Detectors, Predictive models, Prediction algorithms, ethical computer vision BibRef

Osada, G.[Genki], Takahashi, T.[Tsubasa], Ahsan, B.[Budrul], Nishide, T.[Takashi],
Out-of-Distribution Detection with Reconstruction Error and Typicality-based Penalty,
WACV23(5540-5552)
IEEE DOI 2302
Manifolds, Measurement uncertainty, Noise measurement, Reliability, Task analysis, Image reconstruction, adversarial attack and defense methods BibRef

Cai, M.[Mu], Li, Y.X.[Yi-Xuan],
Out-of-distribution Detection via Frequency-regularized Generative Models,
WACV23(5510-5519)
IEEE DOI 2302
Training, Uncertainty, Image synthesis, Measurement uncertainty, Estimation, Particle measurements, Algorithms: Explainable, fair, image and video synthesis BibRef

Wilson, S.[Samuel], Fischer, T.[Tobias], Sünderhauf, N.[Niko], Dayoub, F.[Feras],
Hyperdimensional Feature Fusion for Out-of-Distribution Detection,
WACV23(2643-2653)
IEEE DOI 2302
Visualization, Sensitivity, Neural networks, Detectors, Feature extraction, Computational efficiency, segmentation) BibRef

Hornauer, J.[Julia], Belagiannis, V.[Vasileios],
Heatmap-based Out-of-Distribution Detection,
WACV23(2602-2611)
IEEE DOI 2302
Heating systems, Visualization, Technological innovation, Codes, Neural networks, Decoding, Algorithms: Explainable, fair, ethical computer vision BibRef

Cho, J.[Jeongik], Krzyzak, A.[Adam],
Self-supervised Out-of-Distribution Detection with Dynamic Latent Scale GAN,
SSSPR22(113-121).
Springer DOI 2301
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Fan, J.W.[Jia-Wei], Ou, Z.H.[Zhong-Hong], Yu, X.[Xie], Yang, J.W.[Jun-Wei], Wang, S.[Shigeng], Kang, X.Y.[Xiao-Yang], Zhang, H.X.[Hong-Xing], Song, M.[Meina],
Episodic Projection Network for Out-of-Distribution Detection in Few-shot Learning,
ICPR22(3076-3082)
IEEE DOI 2212
Semantic segmentation, Perturbation methods, Neural networks, Object detection, Feature extraction, Classification algorithms BibRef

Huang, C.Q.[Chao-Qin], Guan, H.Y.[Hao-Yan], Jiang, A.[Aofan], Zhang, Y.[Ya], Spratling, M.W.[Michael W.], Wang, Y.F.[Yan-Feng],
Registration Based Few-Shot Anomaly Detection,
ECCV22(XXIV:303-319).
Springer DOI 2211

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Sun, Y.Y.[Yi-You], Li, Y.X.[Yi-Xuan],
DICE: Leveraging Sparsification for Out-of-Distribution Detection,
ECCV22(XXIV:691-708).
Springer DOI 2211
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Gao, R.[Ruiyuan], Zhao, C.C.[Chen-Chen], Hong, L.Q.[Lan-Qing], Xu, Q.[Qiang],
DiffGuard: Semantic Mismatch-Guided Out-of-Distribution Detection using Pre-trained Diffusion Models,
ICCV23(1579-1589)
IEEE DOI 2401
BibRef

Yang, Y.J.[Yi-Jun], Gao, R.[Ruiyuan], Xu, Q.[Qiang],
Out-of-Distribution Detection with Semantic Mismatch Under Masking,
ECCV22(XXIV:373-390).
Springer DOI 2211
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Pei, S.[Sen], Zhang, X.[Xin], Fan, B.[Bin], Meng, G.F.[Gao-Feng],
Out-of-distribution Detection with Boundary Aware Learning,
ECCV22(XXIV:235-251).
Springer DOI 2211
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Zhao, B.C.[Bing-Chen], Yu, S.Z.[Shao-Zuo], Ma, W.[Wufei], Yu, M.X.[Ming-Xin], Mei, S.X.[Shen-Xiao], Wang, A.T.[Ang-Tian], He, J.[Ju], Yuille, A.L.[Alan L.], Kortylewski, A.[Adam],
OOD-CV: A Benchmark for Robustness to Out-of-Distribution Shifts of Individual Nuisances in Natural Images,
ECCV22(VIII:163-180).
Springer DOI 2211
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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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Qi, J.X.[Jia-Xin], Tang, K.[Kaihua], Sun, Q.[Qianru], Hua, X.S.[Xian-Sheng], Zhang, H.W.[Han-Wang],
Class Is Invariant to Context and Vice Versa: On Learning Invariance for Out-Of-Distribution Generalization,
ECCV22(XXV:92-109).
Springer DOI 2211
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Doorenbos, L.[Lars], Sznitman, R.[Raphael], Márquez-Neila, P.[Pablo],
Data Invariants to Understand Unsupervised Out-of-Distribution Detection,
ECCV22(XXXI:133-150).
Springer DOI 2211
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Albert, P.[Paul], Arazo, E.[Eric], O'Connor, N.E.[Noel E.], McGuinness, K.[Kevin],
Embedding Contrastive Unsupervised Features to Cluster In- And Out-of-Distribution Noise in Corrupted Image Datasets,
ECCV22(XXXI:402-419).
Springer DOI 2211
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Ossonce, M.[Maxime], Alberge, F.[Florence], Duhamel, P.[Pierre],
Joint Classification and out-of-Distribution Detection Based on Structured Latent Space of Variational Auto-Encoders,
ICIP22(1201-1205)
IEEE DOI 2211
Training, Deep learning, Neural networks, Generative adversarial networks, Robustness, Object recognition, Variational auto-encoder BibRef

Benkert, R.[Ryan], Prabhushankar, M.[Mohit], AlRegib, G.[Ghassan],
Forgetful Active Learning with Switch Events: Efficient Sampling for Out-of-Distribution Data,
ICIP22(2196-2200)
IEEE DOI 2211
Training, Protocols, Annotations, Neural networks, Switches, Benchmark testing, Active Learning, Forgetting Events, Out-of-Distribution BibRef

Boonlia, H.[Harshita], Dam, T.[Tanmoy], Ferdaus, M.M.[Md Meftahul], Anavatti, S.G.[Sreenatha G.], Mullick, A.[Ankan],
Improving Self-Supervised Learning for Out-Of-Distribution Task via Auxiliary Classifier,
ICIP22(3036-3040)
IEEE DOI 2211
Training, Head, Codes, Semantics, Self-supervised learning, Multitasking, out of distribution, self-supervised learning, auxiliary classifier BibRef

Mukai, K.[Koki], Kumano, S.[Soichiro], Yamasaki, T.[Toshihiko],
Improving Robustness to out-of-Distribution Data by Frequency-Based Augmentation,
ICIP22(3116-3120)
IEEE DOI 2211
Image recognition, Training data, Receivers, Robustness, Data models, Convolutional neural networks, neural network, data augmentation BibRef

Li, R.[Ruoqi], Zhang, C.Y.[Chong-Yang], Zhou, H.[Hao], Shi, C.[Chao], Luo, Y.[Yan],
Out-of-Distribution Identification: Let Detector Tell Which I Am Not Sure,
ECCV22(X:638-654).
Springer DOI 2211
BibRef

Ndiour, I.J.[Ibrahima J.], Ahuja, N.A.[Nilesh A.], Tickoo, O.[Omesh],
Subspace Modeling for Fast Out-Of-Distribution and Anomaly Detection,
ICIP22(3041-3045)
IEEE DOI 2211
Dimensionality reduction, Deep learning, Uncertainty, Semantics, Neural networks, Memory management, Feature extraction, subspace modeling BibRef

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

Li, L.[Lin], Chen, L.[Long], Huang, Y.F.[Yi-Feng], Zhang, Z.M.[Zhi-Meng], Zhang, S.Y.[Song-Yang], Xiao, J.[Jun],
The Devil is in the Labels: Noisy Label Correction for Robust Scene Graph Generation,
CVPR22(18847-18856)
IEEE DOI 2210
Training, Annotations, Clustering algorithms, Pattern recognition, Noise measurement, Task analysis, Vision+language 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, Pattern recognition, Classification algorithms, Object recognition, Representation learning BibRef

Wang, H.Q.[Hao-Qi], Li, Z.Z.[Zhi-Zhong], Feng, L.[Litong], Zhang, W.[Wayne],
ViM: Out-Of-Distribution with Virtual-Logit Matching,
CVPR22(4911-4920)
IEEE DOI 2210
Codes, Computational modeling, Benchmark testing, Transformers, Feature extraction, Self- semi- meta- unsupervised learning BibRef

Sun, Z.[Zeren], Shen, F.M.[Fu-Min], Huang, D.[Dan], Wang, Q.[Qiong], Shu, X.B.[Xiang-Bo], Yao, Y.Z.[Ya-Zhou], Tang, J.H.[Jin-Hui],
PNP: Robust Learning from Noisy Labels by Probabilistic Noise Prediction,
CVPR22(5301-5310)
IEEE DOI 2210
Deep learning, Training data, Predictive models, Probabilistic logic, Pattern recognition, Noise measurement, Self- semi- meta- unsupervised learning BibRef

Zhou, Y.[Yibo],
Rethinking Reconstruction Autoencoder-Based Out-of-Distribution Detection,
CVPR22(7369-7377)
IEEE DOI 2210
Measurement, Uncertainty, Semantics, Pipelines, Training data, Detectors, Others, Recognition: detection, categorization, Self- semi- meta- unsupervised learning BibRef

Ye, N.Y.[Nan-Yang], Li, K.[Kaican], Bai, H.Y.[Hao-Yue], Yu, R.P.[Run-Peng], Hong, L.Q.[Lan-Qing], Zhou, F.W.[Feng-Wei], Li, Z.G.[Zhen-Guo], Zhu, J.[Jun],
OoD-Bench: Quantifying and Understanding Two Dimensions of Out-of-Distribution Generalization,
CVPR22(7937-7948)
IEEE DOI 2210
Training, Deep learning, Correlation, Codes, Neural networks, Benchmark testing, Transfer/low-shot/long-tail learning, Datasets and evaluation BibRef

Dong, X.[Xin], Guo, J.F.[Jun-Feng], Li, A.[Ang], Ting, W.T.[Wei-Te], Liu, C.[Cong], Kung, H.T.,
Neural Mean Discrepancy for Efficient Out-of-Distribution Detection,
CVPR22(19195-19205)
IEEE DOI 2210
Training, Measurement, Computational modeling, Training data, Detectors, Data models, Pattern recognition, Transfer/low-shot/long-tail learning BibRef

Khalid, U.[Umar], Esmaeili, A.[Ashkan], Karim, N.[Nazmul], Rahnavard, N.[Nazanin],
RODD: A Self-Supervised Approach for Robust Out-of-Distribution Detection,
ArtOfRobust22(163-170)
IEEE DOI 2210
Representation learning, Training, Deep learning, Gaussian noise, Benchmark testing, Feature extraction, Data models BibRef

Guarrera, M.[Matteo], Jin, B.[Baihong], Lin, T.W.[Tung-Wei], Zuluaga, M.A.[Maria A.], Chen, Y.X.[Yu-Xin], Sangiovanni-Vincentelli, A.[Alberto],
Class-wise Thresholding for Robust Out-of-Distribution Detection,
FaDE-TCV22(2836-2845)
IEEE DOI 2210
Training, Deep learning, Neural networks, Training data, Detectors BibRef

Cao, S.[Senqi], Zhang, Z.F.[Zhong-Fei],
Deep Hybrid Models for Out-of-Distribution Detection,
CVPR22(4723-4733)
IEEE DOI 2210
Deep learning, Training, Uncertainty, Statistical analysis, Computational modeling, Training data, Statistical methods BibRef

Wang, R.[Ruoyu], Yi, M.Y.[Ming-Yang], Chen, Z.[Zhitang], Zhu, S.Y.[Sheng-Yu],
Out-of-distribution Generalization with Causal Invariant Transformations,
CVPR22(375-385)
IEEE DOI 2210
Training, Machine learning algorithms, Statistical analysis, Training data, Machine learning, Data models, Statistical methods, Machine 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
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.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

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.W.[Zi-Wei],
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, 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.P.[Jian-Peng], Shi, Y.W.[Ya-Wen], Gu, Z.Q.[Zhao-Quan], 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.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

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, 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
BibRef

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, 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.[Qing], Aizawa, K.[Kiyoharu],
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
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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
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:May 6, 2024 at 15:50:14