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Deviation detection; Gene expression; Genetic algorithm;
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Outlier detection; Distance-based outliers; Disk-based algorithms;
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Outlier detection
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Bovine phlegmon, Car-bike plot, Clustering,
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Image edge detection, Histograms, Robustness, Data visualization,
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Task analysis, Clustering methods, Databases,
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Robust Hierarchical-Optimization RLS Against Sparse Outliers,
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Recursive Least Squares.
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Artificial neural networks, Gaussian distribution, Uncertainty,
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Outlier detection, Ensemble learning, Member selection,
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Anomaly detection, Isolation forest, Anomaly score, Outliers
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A novel method to repair attribute noise in classification problems,
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Correct attribute errors rather than remove samples.
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Semantics, Visualization, Encoding, Training, Task analysis, Manifolds,
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Anomaly detection, Self-supervised learning,
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2112
Supervised Laplacian eigenmaps, Out-of-sample problem,
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2301
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IP(31), 2022, pp. 525-540.
IEEE DOI
2112
Prototypes, Training, Anomaly detection, Task analysis,
Feature extraction, Predictive models, Kernel, Prototypes,
image classification
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EMDQ: Removal of Image Feature Mismatches in Real-Time,
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Impedance matching, Distortion, Feature matching, mismatch removal,
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Affinity Propagation Based on Structural Similarity Index and Local
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RS(14), No. 5, 2022, pp. xx-yy.
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2203
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Sedghi, M.[Mahlagha],
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Sketches by MoSSaRT:
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Elsevier DOI
2203
Data selection.
Representative selection, Gross sparse corruption,
Manifold learning, Reproducing kernel Hilbert spaces
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Selective Nearest Neighbors Clustering,
PRL(155), 2022, pp. 178-185.
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2203
Clustering, Nearest Neighbor, Border Detection,
Border Peeling Clustering, Outlier detection
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Feature-Induced Label Distribution for Learning with Noisy Labels,
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2203
Label Noise, Label Distribution, Semi-supervised Learning, Deep Learning
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Discriminative feature selection with directional outliers correcting
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PR(126), 2022, pp. 108541.
Elsevier DOI
2204
Feature selection, Directional outlier, Redundant features,
Deviation, Supervised method
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Chen, S.X.[Shun-Xing],
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Xiao, G.B.[Guo-Bao],
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CSDA-Net: Seeking reliable correspondences by channel-Spatial
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PR(126), 2022, pp. 108539.
Elsevier DOI
2204
Feature matching, Deep learning, Outlier rejection, Attention mechanism
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Liu, Y.[Yang],
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Unsupervised anomaly detection via dual transformation-aware
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IET-IPR(16), No. 6, 2022, pp. 1657-1668.
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2204
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From Simulated to Visual Data: A Robust Low-Rank Tensor Completion
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CirSysVideo(32), No. 6, June 2022, pp. 3462-3474.
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2206
Tensors, Matrix decomposition, Minimization, Noise reduction,
Data models, Correlation, Computational modeling,
color image inpainting and denoising
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Huang, S.[Sheng],
Huangfu, L.[Luwen],
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IVC(126), 2022, pp. 104548.
Elsevier DOI
2209
Multi-label learning, Out-of-distribution detection,
Image classification, Sparse learning, Label co-occurrence
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Huyan, N.[Ning],
Quan, D.[Dou],
Zhang, X.R.[Xiang-Rong],
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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
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Sparse random projection isolation forest for outlier detection,
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2212
Outlier detection, Anomaly detection, Isolation forest,
Random projection, Sparse random projection
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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
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2212
Rails, Standards, Pollution measurement, Inspection,
Measurement by laser beam, Data models, Manuals, R-H-ICP
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Shi, Z.W.[Zi-Wei],
Xiao, G.B.[Guo-Bao],
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JRA-Net: Joint representation attention network for correspondence
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PR(135), 2023, pp. 109180.
Elsevier DOI
2212
Correspondences, Joint representation, Attention mechanism,
Outlier rejection, Pose estimation
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Bao, J.F.[Jun-Fang],
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An Improved Innovation Robust Outliers Detection Method for Airborne
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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],
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E3 Outlier: a Self-Supervised Framework for Unsupervised Deep Outlier
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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
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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
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Fujiwara, K.[Kent],
Okura, F.[Fumio],
Matsushita, Y.[Yasuyuki],
Shuffled Linear Regression with Outliers in Both Covariates and
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IJCV(131), No. 3, March 2023, pp. 732-751.
Springer DOI
2302
BibRef
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Roads, B.D.[Brett D.],
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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
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van Ginneken, B.[Bram],
Sogancioglu, E.[Ecem],
Murphy, K.[Keelin],
FRODO: An In-Depth Analysis of a System to Reject Outlier Samples
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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
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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
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Chen, Z.[Zhi],
Duan, J.[Jiang],
Kang, L.[Li],
Qiu, G.P.[Guo-Ping],
Supervised Anomaly Detection via Conditional Generative Adversarial
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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
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Arias, L.A.S.[Luis Antonio Souto],
Oosterlee, C.W.[Cornelis W.],
Cirillo, P.[Pasquale],
AIDA: Analytic isolation and distance-based anomaly detection
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PR(141), 2023, pp. 109607.
Elsevier DOI
2306
Outlier detection, Anomaly explanation, Isolation, Distance, Ensemble methods
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Wei, S.X.[Shen-Xing],
Wei, X.[Xing],
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Outlier detection, Deep learning, Point cloud semantic segmentation
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Outlier detection, -nearest neighbors, -NN,
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detect unknown objects without the reliance on an auxiliary datase.
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Training, Uncertainty, Task analysis, Adaptation models, Transportation,
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Out-of-distribution detection,
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Neural networks, Image recognition, Out-of-distribution detection
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Out-of-distribution generalization,
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Feature extraction, Prototypes, Object detection, Detectors,
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Deal with overconfident classification.
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Time series analysis, Feature extraction, Training, Transformers,
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Training, Biomedical imaging, Data augmentation, Task analysis, Data models,
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Feature extraction, Smoothing methods, Training, Data models,
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Cui, P.[Peng],
Nico Challenge: Out-of-distribution Generalization for Image
Recognition Challenges,
CiV22(433-450).
Springer DOI
2304
BibRef
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
BibRef
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
BibRef
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
BibRef
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
BibRef
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
BibRef
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
WWW Link.
BibRef
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
BibRef
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
BibRef
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
BibRef
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
BibRef
Borlino, F.C.[Francesco Cappio],
Bucci, S.[Silvia],
Tommasi, T.[Tatiana],
Semantic Novelty Detection via Relational Reasoning,
ECCV22(XXV:183-200).
Springer DOI
2211
BibRef
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
BibRef
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
BibRef
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
BibRef
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
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 .