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
Upadhyay, U.,
Mukherjee, P.,
Generating Out of Distribution Adversarial Attack Using Latent Space
Poisoning,
SPLetters(28), 2021, pp. 523-527.
IEEE DOI
2103
Training, Perturbation methods,
Smoothing methods, Mathematical model, manifold space
BibRef
Ding, J.Y.[Jia-Yu],
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
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
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
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
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.B.[Long-Bing],
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
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.
DOI Link
2401
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
BibRef
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
Lu, W.[Wang],
Wang, J.D.[Jin-Dong],
Sun, X.W.[Xin-Wei],
Chen, Y.Q.[Yi-Qiang],
Ji, X.Y.[Xiang-Yang],
Yang, Q.[Qiang],
Xie, X.[Xing],
Diversify: A General Framework for Time Series Out-of-Distribution
Detection and Generalization,
PAMI(46), No. 6, June 2024, pp. 4534-4550.
IEEE DOI
2405
Time series analysis, Feature extraction, Training, Transformers,
Task analysis, Representation learning, Optimization, time series
BibRef
Li, S.C.[Si-Cong],
Li, N.[Ning],
Jing, M.[Min],
Ji, C.[Chen],
Cheng, L.[Liang],
Evaluation of Ten Deep-Learning-Based Out-of-Distribution Detection
Methods for Remote Sensing Image Scene Classification,
RS(16), No. 9, 2024, pp. 1501.
DOI Link
2405
BibRef
Zhou, C.F.[Cheng-Feng],
Wang, J.[Jun],
Xiang, S.C.[Sun-Cheng],
Liu, F.[Feng],
Huang, H.F.[He-Feng],
Qian, D.H.[Da-Hong],
A Simple Normalization Technique Using Window Statistics to Improve
the Out-of-Distribution Generalization on Medical Images,
MedImg(43), No. 6, June 2024, pp. 2086-2097.
IEEE DOI
2406
Training, Biomedical imaging, Data augmentation, Task analysis, Data models,
Computational modeling, Histograms, Normalization, multi-center data
BibRef
Sun, Z.H.[Zhuo-Hao],
Qiu, Y.Q.[Yi-Qiao],
Tan, Z.J.[Zhi-Jun],
Zheng, W.S.[Wei-Shi],
Wang, R.X.[Rui-Xuan],
Classifier-Head Informed Feature Masking and Prototype-Based Logit
Smoothing for Out-of-Distribution Detection,
CirSysVideo(34), No. 7, July 2024, pp. 5630-5640.
IEEE DOI
2407
Feature extraction, Smoothing methods, Training, Data models,
Prototypes, Benchmark testing, logit smoothing
BibRef
Xia, G.X.[Guo-Xuan],
Bouganis, C.S.[Christos-Savvas],
Augmenting the Softmax with Additional Confidence Scores for Improved
Selective Classification with Out-of-Distribution Data,
IJCV(132), No. 1, January 2024, pp. 3714-3752.
Springer DOI
2409
BibRef
Earlier:
Augmenting Softmax Information for Selective Classification with
Out-of-distribution Data,
ACCV22(VI:664-680).
Springer DOI
2307
BibRef
Ma, S.J.[Shi-Jie],
Zhu, F.[Fei],
Cheng, Z.[Zhen],
Zhang, X.Y.[Xu-Yao],
Towards trustworthy dataset distillation,
PR(157), 2025, pp. 110875.
Elsevier DOI Code:
WWW Link.
2409
Dataset distillation, Out-of-distribution detection,
Data-efficient learning, Trustworthy learning
BibRef
Zhu, Y.[Yao],
Chen, Y.F.[Yue-Feng],
Li, X.D.[Xiao-Dan],
Zhang, R.[Rong],
Xue, H.[Hui],
Tian, X.[Xiang],
Jiang, R.X.[Rong-Xin],
Zheng, B.L.[Bo-Lun],
Chen, Y.W.[Yao-Wu],
Rethinking Out-of-Distribution Detection From a Human-Centric
Perspective,
IJCV(132), No. 10, October 2024, pp. 4633-4650.
Springer DOI
2410
BibRef
Zhao, B.C.[Bing-Chen],
Wang, J.H.[Jia-Hao],
Ma, W.F.[Wu-Fei],
Jesslen, A.[Artur],
Yang, S.W.[Si-Wei],
Yu, S.Z.[Shao-Zuo],
Zendel, O.[Oliver],
Theobalt, C.[Christian],
Yuille, A.L.[Alan L.],
Kortylewski, A.[Adam],
OOD-CV-v2: An Extended Benchmark for Robustness to
Out-of-Distribution Shifts of Individual Nuisances in Natural Images,
PAMI(46), No. 12, December 2024, pp. 11104-11118.
IEEE DOI
2411
Benchmark testing, Robustness, Shape, Pose estimation, Meteorology,
Noise, Out-of-distribution generalization, Robustness,
6D pose estimation
BibRef
Zhao, B.C.[Bing-Chen],
Yu, S.Z.[Shao-Zuo],
Ma, W.F.[Wu-Fei],
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
He, R.D.[Run-Dong],
Han, Z.Y.[Zhong-Yi],
Nie, X.S.[Xiu-Shan],
Yin, Y.L.[Yi-Long],
Chang, X.J.[Xiao-Jun],
Visual Out-of-Distribution Detection in Open-Set Noisy Environments,
IJCV(132), No. 11, November 2024, pp. 5453-5470.
Springer DOI
2411
BibRef
Wu, Y.W.[Ying-Wen],
Li, T.[Tao],
Cheng, X.W.[Xin-Wen],
Yang, J.[Jie],
Huang, X.L.[Xiao-Lin],
Low-Dimensional Gradient Helps Out-of-Distribution Detection,
PAMI(46), No. 12, December 2024, pp. 11378-11391.
IEEE DOI
2411
Feature extraction, Dimensionality reduction, Data models,
Nearest neighbor methods, Training, Benchmark testing,
deep neural networks (DNNs)
BibRef
Zhang, Y.H.[Yu-Hang],
Hu, J.[Jiani],
Wen, D.C.[Dong-Chao],
Deng, W.H.[Wei-Hong],
Unsupervised evaluation for out-of-distribution detection,
PR(160), 2025, pp. 111212.
Elsevier DOI
2501
Unsupervised evaluation, Out-of-distribution detection
BibRef
Yang, J.K.[Jing-Kang],
Zhou, K.Y.[Kai-Yang],
Li, Y.X.[Yi-Xuan],
Liu, Z.W.[Zi-Wei],
Generalized Out-of-Distribution Detection: A Survey,
IJCV(132), No. 12, December 2024, pp. 5635-5662.
Springer DOI
2501
BibRef
Fang, K.[Kun],
Tao, Q.H.[Qing-Hua],
Huang, X.L.[Xiao-Lin],
Yang, J.[Jie],
Revisiting Deep Ensemble for Out-of-Distribution Detection:
A Loss Landscape Perspective,
IJCV(132), No. 12, December 2024, pp. 6107-6126.
Springer DOI
2501
BibRef
Huang, Z.[Zhuo],
Li, M.Y.[Mu-Yang],
Shen, L.[Li],
Yu, J.[Jun],
Gong, C.[Chen],
Han, B.[Bo],
Liu, T.L.[Tong-Liang],
Winning Prize Comes from Losing Tickets: Improve Invariant Learning by
Exploring Variant Parameters for Out-of-Distribution Generalization,
IJCV(133), No. 1, January 2025, pp. 456-474.
Springer DOI
2501
BibRef
Jia, Y.L.[Yu-Long],
Li, J.M.[Jia-Ming],
Zhao, G.L.[Gan-Long],
Liu, S.Y.[Shuang-Yin],
Sun, W.J.[Wei-Jun],
Lin, L.[Liang],
Li, G.B.[Guan-Bin],
Enhancing out-of-distribution detection via diversified
multi-prototype contrastive learning,
PR(161), 2025, pp. 111214.
Elsevier DOI
2502
Out-of-distribution detection, Robust AI, Contrastive learning
BibRef
Nie, J.[Jun],
Luo, Y.[Yadan],
Ye, S.S.[Shan-Shan],
Zhang, Y.G.[Yong-Gang],
Tian, X.M.[Xin-Mei],
Fang, Z.[Zhen],
Out-of-Distribution Detection with Virtual Outlier Smoothing,
IJCV(133), No. 2, February 2025, pp. 724-741.
Springer DOI
2502
BibRef
Liu, W.F.[Wei-Feng],
Yu, H.R.[Hao-Ran],
Wang, Y.J.[Ying-Jie],
Liu, B.[Baodi],
Tao, D.P.[Da-Peng],
Chen, H.L.[Hong-Long],
IW-ViT: Independence-Driven Weighting Vision Transformer for
out-of-distribution generalization,
PR(161), 2025, pp. 111308.
Elsevier DOI
2502
Out-of-distribution generalization, Vision Transformer,
Independence sample weighting, Feature decorrelation
BibRef
Wei, J.[Jie],
Wang, G.[Guotai],
Zhang, S.T.[Shao-Ting],
Fine-grained medical image out-of-distribution detection through
multi-view feature uncertainty and adversarial sample generation,
PR(162), 2025, pp. 111401.
Elsevier DOI
2503
Out-of-distribution detection, Uncertainty,
Mahalanobis distance, Adversarial samples
BibRef
Song, Y.[Yue],
Wang, W.[Wei],
Sebe, N.[Nicu],
RankFeat&RankWeight: Rank-1 Feature/Weight Removal for
Out-of-Distribution Detection,
PAMI(47), No. 4, April 2025, pp. 2505-2519.
IEEE DOI
2503
Feature extraction, Vectors, Benchmark testing, Sun, Upper bound,
Predictive models, Deep learning, Data models, Training,
out-of-distribution detection
BibRef
Miyai, A.[Atsuyuki],
Yu, Q.[Qing],
Irie, G.[Go],
Aizawa, K.[Kiyoharu],
GL-MCM: Global and Local Maximum Concept Matching for Zero-Shot
Out-of-Distribution Detection,
IJCV(133), No. 6, June 2025, pp. 3586-3596.
Springer DOI
2505
BibRef
Peng, Z.[Zhimao],
Wang, E.[Enguang],
Liu, X.L.[Xia-Lei],
Cheng, M.M.[Ming-Ming],
Predictive Sample Assignment for Semantically Coherent
Out-of-Distribution Detection,
CirSysVideo(35), No. 5, May 2025, pp. 4686-4697.
IEEE DOI Code:
WWW Link.
2505
Training, Data models, Predictive models, Filtering,
Noise measurement, Semantics, Representation learning, robustness
BibRef
Wood, D.[David],
Kapp, D.[David],
Messay-Kebede, T.[Temesguen],
Hirakawa, K.[Keigo],
LMP-GAN: Out-of-Distribution Detection for Non-Control Data Malware
Attacks,
PAMI(47), No. 7, July 2025, pp. 5434-5444.
IEEE DOI
2506
Malware, Anomaly detection, Training, Trojan horses, Training data,
Detectors, Robot sensing systems, Monitoring, semisupervised learning
BibRef
Chen, R.[Ran],
Zhu, H.[Huaguang],
Xiao, B.[Bofei],
Ma, T.[TieFeng],
CAM: Causality-driven Adaptive Sparsity and Hierarchical Memory for
robust out-of-distribution learning in GNNs,
PR(168), 2025, pp. 111812.
Elsevier DOI Code:
WWW Link.
2506
Graph neural networks, Causal inference,
Adaptive sparse expert selection, Environmental shifts
BibRef
Zhu, Y.[Yao],
Yan, X.[Xiu],
Xie, C.L.[Chuan-Long],
Towards Boosting Out-of-Distribution Detection from a Spatial Feature
Importance Perspective,
IJCV(133), No. 7, July 2025, pp. 3839-3857.
Springer DOI
2506
BibRef
Xiang, X.[Xiang],
Xu, Z.[Zhuo],
Zhang, Z.[Zihan],
Zeng, Z.G.[Zhi-Gang],
Chen, X.L.[Xi-Lin],
Enhanced Dual-Pattern Matching With Vision-Language Representation
for Out-of-Distribution Detection,
PAMI(47), No. 11, November 2025, pp. 9673-9687.
IEEE DOI
2510
BibRef
Earlier: A3, A2, A1, Only:
Vision-language Dual-pattern Matching for Out-of-distribution Detection,
ECCV24(LXXXV: 273-291).
Springer DOI
2412
Visualization, Adaptation models, Training, Data models,
Computational modeling, Feature extraction, Pattern matching,
vision-language models
BibRef
Miao, W.J.[Wen-Jun],
Pang, G.S.[Guan-Song],
Nguyen, T.T.[Trong-Tung],
Fang, R.[Ruohuan],
Zheng, J.[Jin],
Bai, X.[Xiao],
OpenCIL: Benchmarking out-of-distribution detection in class
incremental learning,
PR(171), 2026, pp. 112163.
Elsevier DOI Code:
WWW Link.
2510
Out-of-distribution detection, Class incremental learning
BibRef
Li, H.Y.[Hao-Yang],
Wang, X.[Xin],
Zhang, Z.W.[Zi-Wei],
Zhu, W.W.[Wen-Wu],
Out-of-Distribution Generalization on Graphs: A Survey,
PAMI(47), No. 11, November 2025, pp. 10490-10512.
IEEE DOI
2510
Training, Data models, Machine learning, Reviews, Testing, Surveys,
Data augmentation, Self-supervised learning, Pipelines,
out-of-distribution generalization (OOD)
BibRef
Zhu, L.[Lin],
Yang, Y.F.[Yi-Feng],
Nie, Z.C.[Zi-Chao],
Gao, Y.[Yuan],
Li, J.R.[Jia-Rui],
Gu, Q.Y.[Qin-Ying],
Wang, X.B.[Xin-Bing],
Zhou, C.H.[Cheng-Hu],
Ye, N.Y.[Nan-Yang],
InfoBound: A Provable Information-Bounds Inspired Framework for Both
OoD Generalization and OoD Detection,
PAMI(47), No. 11, November 2025, pp. 10227-10242.
IEEE DOI
2510
Semantics, Mutual information, Entropy, Training, Minimization, Hands,
Feature extraction, Data models, Testing, Mathematical models,
bound minimization
BibRef
Ning, J.[Jie],
Sun, J.[Jiebao],
Shi, S.Z.[Sheng-Zhu],
Guo, Z.[Zhichang],
Li, Y.[Yao],
Li, H.W.[Hong-Wei],
Wu, B.Y.[Bo-Ying],
Adversarial Transferability in Deep Denoising Models: Theoretical
Insights and Robustness Enhancement via Out-of-Distribution Typical
Set Sampling,
SIIMS(18), No. 3, 2025, pp. 1788-1827.
DOI Link
2510
BibRef
Huang, K.[Kuiyun],
Chen, M.L.[Meng-Long],
Zheng, H.[Hong],
Lin, B.[Baihong],
Fan, S.[Shicai],
Soft Cluster-Aware Equivariant Contrastive Learning for Unsupervised
Out-of-Distribution Detection,
CirSysVideo(35), No. 11, November 2025, pp. 11309-11322.
IEEE DOI
2511
Semantics, Contrastive learning, Representation learning,
Feature extraction, Training, Data mining, Accuracy, Fans, Prototypes,
semantic information
BibRef
Lu, S.[Shuo],
Wang, Y.S.[Ying-Sheng],
Sheng, L.J.[Li-Jun],
He, L.X.[Ling-Xiao],
Zheng, A.[Aihua],
Liang, J.[Jian],
Out-of-Distribution Detection: A Task-Oriented Survey of Recent
Advances,
Surveys(58), No. 2, September 2025, pp. xx-yy.
DOI Link
2511
Survey, Out-of-Distribution. Trustworthy machine learning, out-of-distribution detection
BibRef
Zhu, L.[Lin],
Yin, W.H.[Wei-Han],
Wu, F.[Fan],
Gu, Q.Y.[Qin-Ying],
Wang, X.B.[Xin-Bing],
Zhou, C.H.[Cheng-Hu],
Ye, N.Y.[Nan-Yang],
Bayes-CAL: Robust Cross-Modal Alignment by Bayesian Approach for
Few-Shot OoD Generalization,
IJCV(133), No. 10, October 2025, pp. 7076-7109.
Springer DOI
2511
BibRef
Yang, Z.[Zhenni],
Liu, C.X.[Cheng-Xu],
Qian, X.M.[Xue-Ming],
SLE: Out-of-Distribution Detection With Shallow Layer-Driven
Enhancement,
MultMed(27), 2025, pp. 8288-8297.
IEEE DOI
2511
Training, Feature extraction, Semantics, Manifold learning,
Redundancy, Detectors, Data mining, Cross layer design,
semantic shifts
BibRef
Zhang, H.[Hanlei],
Zhou, Q.[Qianrui],
Xu, H.[Hua],
Su, J.H.[Jian-Hua],
Evans, R.[Roberto],
Gao, K.[Kai],
Multimodal Classification and Out-of-Distribution Detection for
Multimodal Intent Understanding,
MultMed(27), 2025, pp. 9887-9901.
IEEE DOI
2601
Transformers, Videos, Semantics, Intent recognition,
Feature extraction, Contrastive learning, Benchmark testing,
multimodal fusion
BibRef
Zhang, Y.F.[Yi-Fan],
Wang, X.[Xue],
Zhou, T.[Tian],
Yuan, K.[Kun],
Zhang, Z.[Zhang],
Wang, L.[Liang],
Jin, R.[Rong],
Model-Free Test Time Adaptation for Out-of-Distribution Detection,
PAMI(48), No. 2, February 2026, pp. 1542-1553.
IEEE DOI
2601
Nearest neighbor methods, Adaptation models, Benchmark testing,
Training, Machine learning, Detectors, Data models, Accuracy,
non-parametric classifier
BibRef
Maldonado, C.[Camilo],
Valle, C.[Carlos],
Allende, H.[Héctor],
Bimodal beta mixture distribution for enhanced OOD
inner-differentiation in multi-class text classification,
PRL(200), 2026, pp. 158-164.
Elsevier DOI
2601
Out-of-distribution detection, Natural language processing,
Outlier exposure, Pre-trained transformer models
BibRef
Wen, J.[Jie],
Liu, Y.C.[Yi-Cheng],
Huang, C.[Chao],
Liu, C.L.[Cheng-Liang],
Xu, Y.[Yong],
Cao, X.C.[Xiao-Chun],
Causal Interventional Prompt Tuning for Few-Shot Out-of-Distribution
Generalization,
PAMI(48), No. 2, February 2026, pp. 1978-1991.
IEEE DOI
2601
Training, Adaptation models, Correlation, Feature extraction, Tuning,
Birds, Visualization, Robustness, Image recognition, Data models, CLIP,
Out-of-Distribution generalization
BibRef
Zhu, L.[Lin],
Yin, W.H.[Wei-Han],
Yang, Y.[Yiyao],
Wu, Y.F.[Yi-Fei],
Gu, Q.Y.[Qin-Ying],
Wang, X.[Xinbing],
Zhou, C.H.[Cheng-Hu],
Ye, N.[Nanyang],
CFSM: A Novel Causal Feature Selection Module for Two-Dimensional
Out-of-Distribution Generalization,
PAMI(48), No. 2, February 2026, pp. 1590-1607.
IEEE DOI
2601
Training, Analytical models, Correlation, Architecture, Buildings,
Benchmark testing, Feature extraction, Mathematical models,
causal feature selection
BibRef
Ye, N.Y.[Nan-Yang],
Li, K.[Kaican],
Wu, F.[Fan],
Zhou, J.D.[Jun-Dong],
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],
Wang, X.B.[Xin-Bing],
Zhou, C.H.[Cheng-Hu],
OoDBench+: Quantifying and Understanding Two Dimensions of
Out-of-Distribution Generalization,
PAMI(48), No. 3, March 2026, pp. 2566-2580.
IEEE DOI
2602
BibRef
Earlier: A1, A2, A5, A6, A7, A8, A9, A10, Only:
OoD-Bench: Quantifying and Understanding Two Dimensions of
Out-of-Distribution Generalization,
CVPR22(7937-7948)
IEEE DOI
2210
Correlation, Training, Classification algorithms, Neural networks,
Hands, Deep learning, Benchmark testing, Solid modeling, distribution shifts.
Training, Deep learning, Correlation, Codes, Neural networks,
Benchmark testing, Transfer/low-shot/long-tail learning, Datasets and evaluation
BibRef
Wang, X.[Xin],
Li, H.Y.[Hao-Yang],
Zhang, Z.Y.[Ze-Yang],
Chen, H.B.[Hai-Bo],
Xiao, T.[Tong],
Li, K.[Kehan],
Zhu, W.W.[Wen-Wu],
Uncertainty-Aware Disentangled Dynamic Graph Attention Network for
Out-of-Distribution Generalization,
PAMI(48), No. 3, March 2026, pp. 2200-2220.
IEEE DOI
2602
Uncertainty, Optimization, Training, Synthetic data,
Adaptation models, Accuracy, Testing, Predictive models,
out-of-distribution generalization
BibRef
Sun, Q.Y.[Qing-Yun],
Luo, J.Y.[Jia-Yi],
Yuan, H.N.[Hao-Nan],
Fu, X.C.[Xing-Cheng],
Peng, H.[Hao],
Li, J.X.[Jian-Xin],
Yu, P.S.[Philip S.],
Evolving Graph Learning for Out-of-Distribution Generalization in
Non-Stationary Environments,
PAMI(48), No. 3, March 2026, pp. 2714-2730.
IEEE DOI
2602
Correlation, Testing, Training, Predictive models, Extrapolation,
Encoding, Data models, Convolution, Graph neural networks,
out-of-distribution generalization
BibRef
Ma, X.S.[Xin-Song],
Wu, J.[Jie],
Zou, X.[Xin],
Liu, W.W.[Wei-Wei],
A Unified Decision Rule for Generalized Out-of-Distribution Detection,
PAMI(48), No. 3, March 2026, pp. 3544-3555.
IEEE DOI
2602
Testing, Training, Upper bound, Gaussian distribution,
Feature extraction, Computational modeling, Anomaly detection, P-value
BibRef
Wu, T.S.[Tian-Shuang],
Lyu, S.H.[Shen-Huan],
Wang, Y.Y.[Yan-Yan],
Chen, N.[Ning],
Qu, Z.H.[Zhi-Hao],
Ye, B.[Baoliu],
Compressing model with few class-imbalance samples:
An out-of-distribution expedition,
PRL(201), 2026, pp. 117-124.
Elsevier DOI
2602
Few-shot learning, Network compression, Class imbalance, Image classification
BibRef
Jung, M.C.[Myong Chol],
Dipnall, J.[Joanna],
Gabbe, B.[Belinda],
Zhao, H.[He],
Near OOD Detection for Vision-Language Prompt Learning with Contrastive
Logit Score,
IJCV(134), No. 4, April 2026, pp. 187.
Springer DOI
2603
BibRef
Lind, S.K.[Simon Kristoffersson],
Triebel, R.[Rudolph],
Krüger, V.[Volker],
GPify: Leveraging the Combined Strength of Normalizing Flow and Softmax
For an Out-of-Distribution aware Confidence Score,
IJCV(134), No. 4, April 2026, pp. 185.
Springer DOI
2603
BibRef
Lan, L.[Long],
Hu, Z.H.[Zhao-Hui],
Li, H.[He],
Liu, T.L.[Tong-Liang],
Liu, X.W.[Xin-Wang],
C-WOE: Clustering for Out-of-Distribution Detection Learning With
Wild Outlier Exposure,
IP(35), 2026, pp. 2066-2079.
IEEE DOI
2603
Training, Noise, Reliability, Data models, Computational modeling,
Random variables, Annotations, Weak supervision, Systematics, Solids,
reliable image classification
BibRef
Yu, H.R.[Hao-Ran],
Liu, W.F.[Wei-Feng],
Wang, Y.J.[Ying-Jie],
Liu, B.[Baodi],
Tao, D.P.[Da-Peng],
Chen, H.L.[Hong-Long],
Causal-guided strength differential independence sample weighting for
out-of-distribution generalization,
PR(176), 2026, pp. 113179.
Elsevier DOI
2603
Out-of-distribution generalization, Distribution shift,
Independence sample weighting, Feature decorrelation
BibRef
Yin, Z.[Zihao],
Wang, Z.H.[Zhi-Hai],
Liu, H.Y.[Hai-Yang],
Li, C.L.[Chuan-Lan],
Yao, M.[Muyun],
Li, S.J.[Shi-Jiang],
Li, F.J.[Fang-Jing],
Ren, J.[Jia],
Yang, Y.C.[Yan-Chao],
PUA: Pseudo-features made useful again for robust graph node
classification under distribution shift,
PR(176), 2026, pp. 113185.
Elsevier DOI
2603
Graph neural network, Node classification,
Out-of-distribution generalization, Causal feature disentanglement
BibRef
Kasarla, T.[Tejaswi],
van Spengler, M.[Max],
Mettes, P.S.[Pascal S.],
Balanced Hyperbolic Embeddings Are Natural Out-of-Distribution
Detectors,
IJCV(134), No. 1, January 2026, pp. 202.
Springer DOI
2604
BibRef
Yang, Y.H.[Yan-Hua],
Yang, M.[Muli],
Li, J.H.[Jia-Hua],
Wang, H.[Henan],
Deng, C.[Cheng],
Zhu, H.Y.[Hong-Yuan],
Counterfactual Risk Minimization for Out-of-Distribution
Generalization,
IP(35), 2026, pp. 4253-4268.
IEEE DOI Code:
WWW Link.
2605
Space technology, MIMICs, Millimeter wave integrated circuits,
counterfactual
BibRef
Sevillano-García, I.[Iván],
Luengo, J.[Julián],
Herrera, F.[Francisco],
STOOD-X: Explainable out-of-distribution detection via nonparametric
statistical testing on large-scale datasets,
PR(177), 2026, pp. 113254.
Elsevier DOI
2605
Explainable artificial intelligence, Deep learning, Out-of-distribution
BibRef
Zhang, C.[Chi],
Wang, W.[Wei],
Zhao, Y.[Yao],
Sebe, N.[Nicu],
Song, Y.[Yue],
Sharpness-Aware Fine-Tuning for OOD Detection,
IP(35), 2026, pp. 4064-4077.
IEEE DOI
2605
Speckle, MIMICs, Millimeter wave integrated circuits,
Monolithic integrated circuits, Optical noise, Internet, Videos,
sharpness-aware minimization
BibRef
Azzopardi, G.[George],
Esposito, S.[Sabatino],
Greco, A.[Antonio],
Vento, M.[Mario],
ShapeBlend: Boosting out-of-distribution robustness in image
classification via shape-based blending augmentation,
CVIU(268), 2026, pp. 104768.
Elsevier DOI
2605
Corruption robustness, Data augmentation, Distribution shift,
Network bias, Domain generalization
BibRef
Hu, Z.H.[Zhao-Hui],
Wang, Q.Z.[Qi-Zhou],
Liu, X.W.[Xin-Wang],
Lan, L.[Long],
Han, B.[Bo],
On the Two Facets to Conquer Wild Out-of-Distribution Detection,
PAMI(48), No. 7, July 2026, pp. 8060-8074.
IEEE DOI
2606
Data models, Training, Detectors, Semantics, Predictive models,
Reliability theory, Probability density function,
reliable machine learning
BibRef
Han, R.S.[Rui-Song],
Zhang, J.H.[Jia-Hao],
Zhang, G.F.[Guo-Feng],
Cheng, M.Y.[Ming-Yue],
Zhang, C.Q.[Chang-Qing],
Han, Z.[Zongbo],
Retrieval-augmented prompt for out-of-distribution detection,
PR(179), 2026, pp. 113779.
Elsevier DOI
2606
OOD detection, Retrieval-augmented prompt, Vision-language models
BibRef
Lu, X.H.[Xin-Hua],
Lai, R.[Runhe],
Wu, Y.Q.[Yan-Qi],
Chen, K.H.[Kang-Hao],
Dai, Z.M.[Zhi-Ming],
Zheng, W.S.[Wei-Shi],
Wang, R.X.[Rui-Xuan],
PLNK: Prompt Learning With Neutral Knowledge for Few-Shot
Out-of-Distribution Detection,
CirSysVideo(36), No. 7, July 2026, pp. 9518-9532.
IEEE DOI Code:
WWW Link.
2607
Semantics, Visualization, Data models, Benchmark testing, Training, Videos,
Training data, Natural language processing, Focusing, few-shot learning
BibRef
Vojír, T.[Tomáš],
Šochman, J.[Jan],
Matas, J.G.[Jirí G.],
PixOOD: Pixel-Level Out-of-Distribution Detection,
PAMI(48), No. 9, September 2026, pp. 10682-10694.
IEEE DOI
2608
BibRef
Earlier:
ECCV24(LX: 93-109).
Springer DOI
2412
Filtering, Filters, Pixel, Digital images, Electro-absorption modulators,
Location awareness, expectation maximization
BibRef
Dong, Y.L.[Yi-Lin],
Zhu, T.Y.[Tian-Yun],
Li, X.[Xinde],
Dezert, J.[Jean],
Zhou, R.[Rigui],
Zhu, C.M.[Chang-Ming],
Cao, L.[Lei],
Ge, S.Z.S.[Shu-Zhi Sam],
Quantum Conflict Measurement in Decision Fusion for
Out-of-Distribution Detection,
PAMI(48), No. 9, September 2026, pp. 10551-10569.
IEEE DOI
2608
Filtering, Filters, Quantum circuit,
Communication systems,
out-of-distribution detection
BibRef
Fang, K.[Kun],
Tao, Q.H.[Qing-Hua],
He, M.Z.[Ming-Zhen],
Lv, K.[Kexin],
Yang, R.Z.[Run-Ze],
Hu, H.B.[Hai-Bo],
Huang, X.L.[Xiao-Lin],
Yang, J.[Jie],
Cao, L.B.[Long-Bing],
Kernel PCA for Out-of-Distribution Detection: Non-Linear Kernel
Selection and Approximation,
PAMI(48), No. 10, October 2026, pp. 13220-13235.
IEEE DOI
2609
Signal detection, Kernel, Modeling, Training, Distance measurement,
Principal component analysis, Manganese, Machine learning,
Nyström method
BibRef
Shen, X.[Xu],
Liu, Y.X.[Yi-Xin],
Wang, Y.[Yili],
Miao, R.[Rui],
Dai, Y.W.[Yi-Wei],
Pan, S.R.[Shi-Rui],
Chang, Y.[Yi],
Wang, X.[Xin],
Raising the Bar in Graph OOD Generalization: Invariant Learning
Beyond Explicit Environment Modeling,
PAMI(48), No. 10, October 2026, pp. 13339-13353.
IEEE DOI
2609
Prototypes, Modeling, Labeling, Graph neural networks,
Information processing, Machine learning, Training, hyperspherical space
BibRef
Rabbi, R.[Rawhatur],
Sarwar, F.A.[Fabliha Afaf],
Islam, M.F.[Md Farhadul],
Ahmed, T.[Tashik],
Rudro, S.[Sidu],
Rahman, M.M.[Md. Mahfujur],
Manab, M.A.[Meem Arafat],
Mukta, J.N.[Jannatun Noor],
Contrast Then Confidence C^2: Contrastive Pretraining for
Uncertainty-Aware Out-of-Distribution Detection in Satellite Imagery,
CVGeoSpatial26(877-885)
IEEE DOI Code:
WWW Link.
2609
Modeling, Uncertainty, Signal detection, Training, Architecture,
Computer architecture, Entropy, Measurement,
uncertainty estimation
BibRef
Politowicz, A.[Alexander],
Mazumder, S.[Sahisnu],
Liu, B.[Bing],
Improving Out-of-Distribution Detection using Segmented Images and
Cross-View Attention Fusion,
WACV26(5269-5279)
IEEE DOI Code:
WWW Link.
2609
LoRa, Cloud computing, Protocols,
Media Access Control, Receivers, out-of-distribution detection
BibRef
Krumpl, G.[Gerhard],
Avenhaus, H.[Henning],
Possegger, H.[Horst],
One Model, Many Behaviors: Training-Induced Effects on
Out-of-Distribution Detection,
WACV26(4128-4138)
IEEE DOI
2609
Filtering, Filters, Protocols,
Receivers, Media Access Control, out-of-distribution detection
BibRef
Adaloglou, N.[Nikolas],
Petrusheva, D.[Diana],
Asker, M.[Mohamed],
Michels, F.[Felix],
Kollmann, M.[Markus],
ClusterMine: Robust Label-Free Visual Out-Of-Distribution Detection
via Concept Mining from Text Corpora,
WACV26(1999-2010)
IEEE DOI Code:
WWW Link.
2609
Filtering, Filters, Protocols, Pixel,
Learning (artificial intelligence),
negative label mining
BibRef
Wang, Y.[Yimu],
Riddell, E.[Evelien],
Chow, A.[Adrian],
Sedwards, S.[Sean],
Czarnecki, K.[Krzysztof],
Mitigating the Modality Gap: Few-Shot Out-of-Distribution Detection
with Multi-modal Prototypes and Image Bias Estimation,
WACV26(2741-2751)
IEEE DOI
2609
Receivers, Pixel, Protocols, Media Access Control,
Storage area networks, Videos, Video equipment, Communication equipment
BibRef
Sazzad Sayyed, A.Q.M.,
Bastian, N.D.[Nathaniel D.],
Restuccia, F.[Francesco],
ENCORE: A Neural Collapse Perspective on Out-of-Distribution
Detection in Deep Neural Networks,
WACV26(2944-2953)
IEEE DOI
2609
Protocols,
Receivers, Amplitude shift keying, Amplitude modulation,
neural collapse
BibRef
Hayakawa, K.[Kaede],
Maeda, K.[Keisuke],
Togo, R.[Ren],
Ogawa, T.[Takahiro],
Haseyama, M.[Miki],
Out-of-Distribution Sample Selection Generated by Diffusion Model
toward Model Generalization,
ICIP25(1-6)
IEEE DOI
2601
Training, Image recognition, Semantics, Training data, Diffusion models,
Data models, Robustness, Photorealistic images, data screening
BibRef
Noda, S.[Shiho],
Miyai, A.[Atsuyuki],
Yu, Q.[Qing],
Irie, G.[Go],
Aizawa, K.[Kiyoharu],
A Benchmark and Evaluation for Real-World Out-of-Distribution
Detection Using Vision-Language Models,
ICIP25(181-186)
IEEE DOI Code:
WWW Link.
2601
Codes, Semantics, Benchmark testing, Feature extraction, Robustness,
Safety, Out-of-distribution Detection, Vision Language Model, Benchmark
BibRef
Ravikumar, D.[Deepak],
Soufleri, E.[Efstathia],
Roy, K.[Kaushik],
Improved Out-of-Distribution Detection with Additive Angular Margin
Loss,
WiCV25(3425-3432)
IEEE DOI Code:
WWW Link.
2512
Training, Measurement, Additives, Codes, Detectors,
Artificial neural networks, Calibration, Active appearance model,
additive angular margin
BibRef
Guo, H.Z.[Hong-Zhi],
Schrader, P.T.[Paul T.],
Blasch, E.[Erik],
Enhancing Multi-Modal Automatic Target Recognition Using
Out-of-Distribution Exploitation (MATRODE),
PBVS25(4512-4520)
IEEE DOI
2512
Training, Radio frequency, Passive radar, Accuracy,
Target recognition, Computational modeling, Predictive models,
Electro-optical waveguides
BibRef
Chen, G.Y.[Guang-Yao],
Horstmann, K.[Kai],
Wang, Z.L.[Zhi-Ling],
You, F.Q.[Feng-Qi],
Automated Essential Concept Discovery for Few-Shot
Out-of-Distribution Detection,
VAND25(3964-3974)
IEEE DOI
2512
Training, Learning systems, Visual analytics, Prototypes,
Documentation, Benchmark testing, Feature extraction, Standards
BibRef
Khanna, A.[Amol],
Ling, C.[Chenyi],
Everett, D.[Derek],
Raff, E.[Edward],
Inkawhich, N.[Nathan],
Multi-Layer Radial Basis Function Networks for Out-of-Distribution
Detection,
VAND25(3954-3963)
IEEE DOI
2512
Training, Manifolds, Training data, Radial basis function networks,
Computer architecture, Nonhomogeneous media, Feature extraction,
depression mechanism
BibRef
Ekim, B.[Burak],
Tadesse, G.A.[Girmaw Abebe],
Robinson, C.[Caleb],
Hacheme, G.[Gilles],
Schmitt, M.[Michael],
Dodhia, R.[Rahul],
Ferres, J.M.L.[Juan M. Lavista],
Distribution Shifts at Scale: Out-of-distribution Detection in Earth
Observation,
FGVC25(2256-2265)
IEEE DOI Code:
WWW Link.
2512
Earth, Training, Technological innovation, Limiting,
Computational modeling, Semantics, Robustness, Geospatial analysis,
geospatial machine learning
BibRef
Botschen, T.[Thomas],
Kirchheim, K.[Konstantin],
Ortmeier, F.[Frank],
Out-of-Distribution Detection with Adversarial Outlier Exposure,
SAIAD25(4391-4400)
IEEE DOI Code:
WWW Link.
2512
Training, Image segmentation, Perturbation methods,
Object detection, Machine learning, Robustness,
deep learning
BibRef
Schmidt, S.[Sebastian],
Schenk, L.[Leonard],
Schwinn, L.[Leo],
Günnemann, S.[Stephan],
Joint Out-of-Distribution Filtering and Data Discovery Active
Learning,
CVPR25(25677-25687)
IEEE DOI
2508
Training, Measurement, Deep learning, Filtering,
Semantic segmentation, Active learning, Object detection,
category discovery
BibRef
Gutbrod, M.[Max],
Rauber, D.[David],
Nunes, D.W.[Danilo Weber],
Palm, C.[Christoph],
OpenMIBOOD: Open Medical Imaging Benchmarks for Out-Of-Distribution
Detection,
CVPR25(25874-25886)
IEEE DOI Code:
WWW Link.
2508
Training, Image segmentation, Technological innovation, Semantics,
Medical services, Benchmark testing, Reliability,
trustworthy artificial intelligence
BibRef
Chen, Q.[Qi],
Ding, H.[Hu],
Dual Energy-Based Model with Open-World Uncertainty Estimation for
Out-of-distribution Detection,
CVPR25(25728-25737)
IEEE DOI
2508
Uncertainty, Limiting, Scalability, Estimation, Machine learning,
Linear programming, Standards, energy-based model, out-of-distribution detection
BibRef
Yang, Y.F.[Yi-Feng],
Zhu, L.[Lin],
Sun, Z.[Zewen],
Liu, H.[Hengyu],
Gu, Q.Y.[Qin-Ying],
Ye, N.[Nanyang],
OODD: Test-time Out-of-Distribution Detection with Dynamic Dictionary,
CVPR25(30630-30639)
IEEE DOI Code:
WWW Link.
2508
Training, Deep learning, Adaptation models, Dictionaries,
Uncertainty, Computational modeling, Benchmark testing
BibRef
Ye, C.K.[Chang-Kun],
Tsuchida, R.[Russell],
Petersson, L.[Lars],
Barnes, N.M.[Nick M.],
Open Set Label Shift with Test Time Out-of-Distribution Reference,
CVPR25(30619-30629)
IEEE DOI Code:
WWW Link.
2508
Adaptation models, Maximum likelihood estimation,
Analytical models, Codes, Computational modeling,
BibRef
Kim, J.[Jeonghyeon],
Hwang, S.[Sangheum],
Enhanced OoD Detection through Cross-Modal Alignment of Multi-Modal
Representations,
CVPR25(29979-29988)
IEEE DOI
2508
Training, Maximum likelihood estimation, Accuracy, Semantics,
Contrastive learning, Benchmark testing, Tuning,
energy-based model
BibRef
Karunanayake, N.[Naveen],
Seneviratne, S.[Suranga],
Chawla, S.[Sanjay],
CRAFT: Class Ranking Aware Fine-Tuning for Enhanced
Out-of-Distribution Detection,
WACV25(4119-4128)
IEEE DOI
2505
Accuracy, Artificial neural networks, Artificial intelligence,
Autonomous vehicles, out-of-distribution detection, deep neural networks
BibRef
Zhdanov, M.[Maksim],
Dereka, S.[Stanislav],
Kolesnikov, S.[Sergey],
Identity Curvature Laplace Approximation for Improved
Out-of-Distribution Detection,
WACV25(7019-7028)
IEEE DOI
2505
Uncertainty, Estimation, Minimization, Calibration, Bayes methods,
Artificial intelligence, Standards, bayesian methods, calibration
BibRef
Udayangani, N.[Nimeshika],
Dolatabadi, H.M.[Hadi M.],
Erfani, S.[Sarah],
Leckie, C.[Christopher],
Exploiting Inter-Sample Information for Long-Tailed
Out-of-Distribution Detection,
WACV25(8546-8555)
IEEE DOI
2505
Representation learning, Heavily-tailed distribution, Accuracy,
Graph convolutional networks, Training data,
graph representation learning
BibRef
Sinhamahapatra, P.[Poulami],
Schwaiger, F.[Franziska],
Bose, S.[Shirsha],
Wang, H.Y.[Hui-Yu],
Roscher, K.[Karsten],
Günnemann, S.[Stephan],
Finding Dino: A Plug-and-Play Framework for Zero-Shot Detection of
Out-of-Distribution Objects Using Prototypes,
WACV25(8474-8483)
IEEE DOI
2505
Training, Rails, Measurement, Foundation models, Roads, Prototypes,
Object detection, Benchmark testing, Feature extraction, automated driving
BibRef
Fu, H.[Hao],
Patel, N.[Naman],
Krishnamurthy, P.[Prashanth],
Khorrami, F.[Farshad],
CLIPScope: Enhancing Zero-Shot OOD Detection with Bayesian Scoring,
WACV25(5346-5355)
IEEE DOI Code:
WWW Link.
2505
Codes, Foundation models, Databases, Machine learning,
Benchmark testing, Bayes methods, clip,
zero-shot
BibRef
Bonato, J.[Jacopo],
Cotogni, M.[Marco],
Sabetta, L.[Luigi],
Is Retain Set All You Need in Machine Unlearning? Restoring Performance
of Unlearned Models with Out-of-distribution Images,
ECCV24(I: 1-19).
Springer DOI
2412
BibRef
Sharifi, S.[Sina],
Entesari, T.[Taha],
Safaei, B.[Bardia],
Patel, V.M.[Vishal M.],
Fazlyab, M.[Mahyar],
Gradient-regularized Out-of-distribution Detection,
ECCV24(XIII: 459-478).
Springer DOI
2412
BibRef
Aathreya, S.[Saandeep],
Canavan, S.[Shaun],
FLOWCON: Out-of-distribution Detection Using Flow-based Contrastive
Learning,
ECCV24(XVI: 192-209).
Springer DOI
2412
BibRef
Hu, J.J.[Jin-Jing],
Liu, W.R.[Wen-Rui],
Chang, H.[Hong],
Ma, B.P.[Bing-Peng],
Shan, S.G.[Shi-Guang],
Chen, X.L.[Xi-Lin],
An Information Theoretical View for Out-of-distribution Detection,
ECCV24(LV: 418-435).
Springer DOI
2412
BibRef
Wallin, E.[Erik],
Svensson, L.[Lennart],
Kahl, F.[Fredrik],
Hammarstrand, L.[Lars],
Prosub: Probabilistic Open-set Semi-supervised Learning with
Subspace-based Out-of-distribution Detection,
ECCV24(LXI: 129-147).
Springer DOI
2412
BibRef
Nguyen, B.[Bac],
Uhlich, S.[Stefan],
Cardinaux, F.[Fabien],
Mauch, L.[Lukas],
Edraki, M.[Marzieh],
Courville, A.[Aaron],
Saft: Towards Out-of-distribution Generalization in Fine-tuning,
ECCV24(LXIX: 138-154).
Springer DOI
2412
BibRef
Liu, X.X.[Xi-Xi],
Zach, C.[Christopher],
Tag: Text Prompt Augmentation for Zero-shot Out-of-distribution
Detection,
ECCV24(LXXIII: 364-380).
Springer DOI
2412
BibRef
Galesso, S.[Silvio],
Schröppel, P.[Philipp],
Driss, H.[Hssan],
Brox, T.[Thomas],
Diffusion for Out-of-distribution Detection on Road Scenes and Beyond,
ECCV24(LXXIV: 110-126).
Springer DOI
2412
BibRef
Jung, Y.G.[Yoon Gyo],
Park, J.[Jaewoo],
Dong, X.[Xingbo],
Park, H.[Hojin],
Teoh, A.B.J.[Andrew Beng Jin],
Camps, O.[Octavia],
Face Reconstruction Transfer Attack as Out-of-distribution
Generalization,
ECCV24(LXXV: 396-413).
Springer DOI
2412
BibRef
Zhang, Y.B.[Ya-Bin],
Zhu, W.J.[Wen-Jie],
He, C.H.[Chen-Hang],
Zhang, L.[Lei],
LAPT: Label-driven Automated Prompt Tuning for OOD Detection with
Vision-language Models,
ECCV24(LXXII: 271-288).
Springer DOI
2412
BibRef
Le Bellier, G.[Georges],
Audebert, N.[Nicolas],
Detecting Out-Of-Distribution Earth Observation Images with Diffusion
Models,
EarthVision24(481-491)
IEEE DOI
2410
Training, Visualization, Tail, Diffusion models, Data models,
Satellite images, remote sensing,
out-of-distribution detection
BibRef
Kaushik, P.[Prakhar],
Kortylewski, A.[Adam],
Yuille, A.L.[Alan L.],
A Bayesian Approach to OOD Robustness in Image Classification,
CVPR24(17459-17469)
IEEE DOI
2410
Dictionaries, Neural networks, Benchmark testing, Robustness,
Vectors, Bayes methods, unsupervised, domain adaptation, analysis-by-synthesis
BibRef
Piao, Y.H.[Yin-Hua],
Lee, S.[Sangseon],
Lu, Y.X.[Yijing-Xiu],
Kim, S.[Sun],
Improving Out-of-Distribution Generalization in Graphs via
Hierarchical Semantic Environments,
CVPR24(27621-27630)
IEEE DOI
2410
Training, Attention mechanisms, Computational modeling, Semantics,
Stochastic processes,
Molecular Representation Learning
BibRef
Song, Q.Y.[Qing-Yu],
Lin, W.[Wei],
Wang, J.C.[Jun-Cheng],
Xu, H.[Hong],
Towards Robust Learning to Optimize with Theoretical Guarantees,
CVPR24(27488-27496)
IEEE DOI Code:
WWW Link.
2410
Wireless communication, Sufficient conditions,
Computational modeling, Mathematical models, Robustness,
Out-of-distribution generalization
BibRef
Regmi, S.[Sudarshan],
Panthi, B.[Bibek],
Ming, Y.F.[Yi-Fei],
Gyawali, P.K.[Prashnna K],
Stoyanov, D.[Danail],
Bhattarai, B.[Binod],
ReweightOOD: Loss Reweighting for Distance-based OOD Detection,
FaDE-TCV24(131-141)
IEEE DOI
2410
Measurement, Neural networks, Benchmark testing, Safety,
Out-of-Distribution detection,
Reweighted Loss
BibRef
Regmi, S.[Sudarshan],
Panthi, B.[Bibek],
Dotel, S.[Sakar],
Gyawali, P.K.[Prashnna K],
Stoyanov, D.[Danail],
Bhattarai, B.[Binod],
T2FNorm: Train-time Feature Normalization for OOD Detection in Image
Classification,
FaDE-TCV24(153-162)
IEEE DOI
2410
Training, Measurement, Deep learning, Accuracy, Neural networks,
Out-of-Distribution Detection, Normalization
BibRef
Qutub, S.S.[Syed Sha],
Paulitsch, M.[Michael],
Scholl, K.U.[Kay-Ulrich],
Cihangir, N.K.[Neslihan Kose],
Hagn, K.[Korbinian],
Oboril, F.[Fabian],
Hinz, G.[Gereon],
Knoll, A.[Alois],
Situation Monitor: Diversity-Driven Zero-Shot Out-of-Distribution
Detection using Budding Ensemble Architecture for Object Detection,
SAIAD24(3502-3511)
IEEE DOI
2410
Training, Adaptation models, Zero-shot learning,
Computational modeling, Scalability,
BEA
BibRef
Hogeweg, L.E.[Laurens E.],
Gangireddy, R.[Rajesh],
Brunink, D.[Django],
Kalkman, V.J.[Vincent J.],
Cornelissen, L.[Ludo],
Kamminga, J.W.[Jacob W.],
COOD: Combined out-of-distribution detection using multiple measures
for anomaly & novel class detection in large-scale hierarchical
classification,
VAND24(3971-3980)
IEEE DOI
2410
Image quality, Image recognition, Databases,
Biological system modeling, Media, anomaly, novel class, image recognition
BibRef
Choi, D.[Dasol],
Na, D.B.[Dong-Bin],
DMR: Disentangling Marginal Representations for Out-of-Distribution
Detection,
VAND24(4032-4041)
IEEE DOI Code:
WWW Link.
2410
Training, Source coding, Training data, Predictive models, Benchmark testing
BibRef
Liao, C.[Christopher],
Tsiligkaridis, T.[Theodoros],
Kulis, B.[Brian],
Descriptor and Word Soups Q: Overcoming the Parameter Efficiency
Accuracy Tradeoff for Out-of-Distribution Few-shot Learning,
CVPR24(27005-27015)
IEEE DOI
2410
Training, Accuracy, Codes, Memory management, Training data,
Graphics processing units
BibRef
Yuan, Y.[Yue],
He, R.D.[Run-Dong],
Dong, Y.C.[Yi-Cong],
Han, Z.Y.[Zhong-Yi],
Yin, Y.L.[Yi-Long],
Discriminability-Driven Channel Selection for Out-of-Distribution
Detection,
CVPR24(26171-26180)
IEEE DOI
2410
Channel estimation, Estimation, Training data, Machine learning,
Benchmark testing
BibRef
Fan, K.[Ke],
Liu, T.[Tong],
Qiu, X.Y.[Xing-Yu],
Wang, Y.K.[Yi-Kai],
Huai, L.[Lian],
Shangguan, Z.[Zeyu],
Gou, S.[Shuang],
Liu, F.J.[Feng-Jian],
Fu, Y.Q.[Yu-Qian],
Fu, Y.W.[Yan-Wei],
Jiang, X.Q.[Xing-Qun],
Test-Time Linear Out-of-Distribution Detection,
CVPR24(23752-23761)
IEEE DOI Code:
WWW Link.
2410
Training, Sufficient conditions, Neural networks,
Linear regression, Feature extraction, Test-Time Adaptation
BibRef
Humblot-Renaux, G.[Galadrielle],
Escalera, S.[Sergio],
Moeslund, T.B.[Thomas B.],
A Noisy Elephant in the Room: Is Your out-of-Distribution Detector
Robust to Label Noise?,
CVPR24(22626-22636)
IEEE DOI Code:
WWW Link.
2410
Training, Uncertainty, Computational modeling, Noise, Detectors,
Robustness, Out-of-distribution detection, Noisy labels, Image classification
BibRef
Li, T.Q.[Tian-Qi],
Pang, G.S.[Guan-Song],
Bai, X.[Xiao],
Miao, W.J.[Wen-Jun],
Zheng, J.[Jin],
Learning Transferable Negative Prompts for Out-of-Distribution
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CVPR24(17584-17594)
IEEE DOI Code:
WWW Link.
2410
Training, Learning systems, Codes, Computational modeling, Semantics, Lead
BibRef
Xue, F.[Feng],
He, Z.[Zi],
Zhang, Y.[Yuan],
Xie, C.L.[Chuan-Long],
Li, Z.G.[Zhen-Guo],
Tan, F.[Falong],
Enhancing the Power of OOD Detection via Sample-Aware Model Selection,
CVPR24(17148-17157)
IEEE DOI
2410
Computational modeling, Detectors, Benchmark testing,
Classification algorithms
BibRef
Bai, Y.C.[Yi-Chen],
Han, Z.[Zongbo],
Cao, B.[Bing],
Jiang, X.H.[Xiao-Heng],
Hu, Q.H.[Qing-Hua],
Zhang, C.Q.[Chang-Qing],
ID-like Prompt Learning for Few-Shot Out-of-Distribution Detection,
CVPR24(17480-17489)
IEEE DOI Code:
WWW Link.
2410
Training, Learning systems, Focusing, Feature extraction,
Data models, Out-of-Distribution Detection, Few-Shot Prompt Learning
BibRef
Xiao, Z.[Zehao],
Shen, J.Y.[Jia-Yi],
Derakhshani, M.M.[Mohammad Mahdi],
Liao, S.C.[Sheng-Cai],
Snoek, C.G.M.[Cees G. M.],
Any-Shift Prompting for Generalization Over Distributions,
CVPR24(13849-13860)
IEEE DOI
2410
Training, Learning systems, Costs,
Probabilistic logic, Transformers, vision-language,
out-of-distribution generalization
BibRef
Yang, Y.W.[Yi-Wei],
Liu, A.Z.[Anthony Z.],
Wolfe, R.[Robert],
Caliskan, A.[Aylin],
Howe, B.[Bill],
Label-Efficient Group Robustness via Out-of-Distribution Concept
Curation,
CVPR24(12426-12434)
IEEE DOI
2410
Training, Correlation, Accuracy, Training data, Minimization,
Robustness
BibRef
Tang, K.[Keke],
Hou, C.[Chao],
Peng, W.L.[Wei-Long],
Chen, R.[Runnan],
Zhu, P.[Peican],
Wang, W.P.[Wen-Ping],
Tian, Z.H.[Zhi-Hong],
CORES: Convolutional Response-based Score for Out-of-distribution
Detection,
CVPR24(10916-10925)
IEEE DOI
2410
Backtracking, Correlation, Fitting, Artificial neural networks,
Security
BibRef
Zolfi, A.[Alon],
Amit, G.[Guy],
Baras, A.[Amit],
Koda, S.[Satoru],
Morikawa, I.[Ikuya],
Elovici, Y.[Yuval],
Shabtai, A.[Asaf],
YolOOD: Utilizing Object Detection Concepts for Multi-Label
Out-of-Distribution Detection,
CVPR24(5788-5797)
IEEE DOI
2410
YOLO, Computational modeling, Detectors, Machine learning,
Benchmark testing, Data models, out-of-distribution detection,
multi-label classification
BibRef
Baek, E.[Eunsu],
Park, K.[Keondo],
Kim, J.[Jiyoon],
Kim, H.S.[Hyung-Sin],
Unexplored Faces of Robustness and Out-of-Distribution:
Covariate Shifts in Environment and Sensor Domains,
CVPR24(22294-22303)
IEEE DOI Code:
WWW Link.
2410
Prevention and mitigation, Perturbation methods, Digital images,
Computational modeling, Benchmark testing, Cameras, Datasets,
Out-of-Distribution
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
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
BibRef
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
BibRef
Li, J.L.[Jing-Lun],
Zhou, X.Y.[Xin-Yu],
Guo, P.[Pinxue],
Sun, Y.X.[Yi-Xuan],
Huang, Y.W.[Yi-Wen],
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
BibRef
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
BibRef
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
BibRef
Ojaswee,
Agarwal, A.[Akshay],
Ratha, N.[Nalini],
Benchmarking Image Classifiers for Physical Out-of-Distribution
Examples Detection,
OutDistri23(4429-4437)
IEEE DOI
2401
BibRef
Cultrera, L.[Luca],
Seidenari, L.[Lorenzo],
del Bimbo, A.[Alberto],
Leveraging Visual Attention for out-of-distribution Detection,
OutDistri23(4449-4458)
IEEE DOI
2401
BibRef
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
BibRef
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
BibRef
Averly, R.[Reza],
Chao, W.L.[Wei-Lun],
Unified Out-Of-Distribution Detection: A Model-Specific Perspective,
ICCV23(1453-1463)
IEEE DOI
2401
BibRef
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
BibRef
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
BibRef
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
BibRef
Tomáš, V.[Vojíir],
Šochman, J.[Jan],
Aljundi, R.[Rahaf],
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Calibrated Out-of-Distribution Detection with a Generic
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Uncertainty23(4509-4518)
IEEE DOI Code:
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2401
BibRef
Martins, N.P.[Nuno Pimpão],
Kalaidzidis, Y.[Yannis],
Zerial, M.[Marino],
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DeepContrast: Deep Tissue Contrast Enhancement using Synthetic Data
Degradations and OOD Model Predictions,
BioIm23(3830-3839)
IEEE DOI
2401
BibRef
Wu, A.[Aming],
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Deep Feature Deblurring Diffusion for Detecting Out-of-Distribution
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ICCV23(13335-13345)
IEEE DOI Code:
WWW Link.
2401
BibRef
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
BibRef
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
BibRef
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
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Springer DOI
2312
BibRef
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
BibRef
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
BibRef
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
BibRef
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
BibRef
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
BibRef
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
BibRef
Zhang, Z.H.[Zi-Han],
Xiang, X.[Xiang],
Decoupling MaxLogit for Out-of-Distribution Detection,
CVPR23(3388-3397)
IEEE DOI
2309
BibRef
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
BibRef
Liu, Q.H.[Qi-Hao],
Kortylewski, A.[Adam],
Yuille, A.L.[Alan L.],
PoseExaminer: Automated Testing of Out-of-Distribution Robustness in
Human Pose and Shape Estimation,
CVPR23(672-681)
IEEE DOI
2309
BibRef
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
BibRef
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
BibRef
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
BibRef
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
BibRef
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
BibRef
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
BibRef
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
BibRef
Humblot-Renaux, G.[Galadrielle],
Escalera, S.[Sergio],
Moeslund, T.B.[Thomas B.],
Beyond AUROC and co. for evaluating out-of-distribution detection
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SAIAD23(3881-3890)
IEEE DOI
2309
BibRef
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
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2307
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Cohen, N.[Niv],
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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
BibRef
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
BibRef
Liu, H.Z.[Hao-Zhe],
Zhang, W.T.[Wen-Tian],
Xie, J.H.[Jin-Heng],
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.J.[Zhuo-Jun],
Yang, H.[Hua],
Yang, Y.T.[Yu-Ting],
Bao, Q.Y.[Qian-Yue],
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
Zhang, J.Y.[Jing-Yang],
Inkawhich, N.[Nathan],
Linderman, R.[Randolph],
Chen, Y.R.[Yi-Ran],
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
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
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
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],
Learning Non-Linear Invariants for Unsupervised Out-of-distribution
Detection,
ECCV24(LII: 310-327).
Springer DOI
2412
BibRef
Earlier:
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],
Al Regib, 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
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
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
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,
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.Q.[Sen-Qi],
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.Y.[Ruo-Yu],
Yi, M.Y.[Ming-Yang],
Chen, Z.T.[Zhi-Tang],
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
Diers, J.[Jan],
Pigorsch, C.[Christian],
Out-of-Distribution Detection Using Outlier Detection Methods,
CIAP22(III:15-26).
Springer DOI
2205
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, 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
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
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, 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
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
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
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
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
Chapter on Pattern Recognition, Clustering, Statistics, Grammars, Learning, Neural Nets, Genetic Algorithms continues in
Outlier Rejection, Outlier Removal .