The DOI matches its Crossref record; 42 of the 45 references it cites were found in a registry or an index.
Generalized ODIN: Detecting Out-of-Distribution Image Without Learning From Out-of-Distribution Data
Open accessAbstract
Deep neural networks have attained remarkable performance when applied to data that comes from the same distribution as that of the training set, but can significantly degrade otherwise. Therefore, detecting whether an example is out-of-distribution (OoD) is crucial to enable a system that can reject such samples or alert users. Recent works have made significant progress on OoD benchmarks consisting of small image datasets. However, many recent methods based on neural networks rely on training or tuning with both in-distribution and out-of-distribution data. The latter is generally hard to define a-priori, and its selection can easily bias the learning. We base our work on a popular method ODIN, proposing two strategies for freeing it from the needs of tuning with OoD data, while improving its OoD detection performance. We specifically propose to decompose confidence scoring as well as a modified input pre-processing method. We show that both of these significantly help in detection performance. Our further analysis on a larger scale image dataset shows that the two types of distribution shifts, specifically semantic shift and non-semantic shift, present a significant difference in the difficulty of the problem, providing an analysis of when ODIN-like strategies do or do not work.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“The perturbation magnitude is determined on a small held-out validation set.”
From QUTE: Quantifying Uncertainty in TinyML with Early-exit-assisted ensembles for model-monitoring (2024) · cites this paper as Hsu et al., 2020
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“This suggests that general OOD-detectors are not automatically effective for CID detection”
From QUTE: Quantifying Uncertainty in TinyML with Early-exit-assisted ensembles for model-monitoring (2024) · cites this paper as Hsu et al., 2020
Experimental — accuracy not yet measuredThe cited source says what the claim attributes to it.
In the citing paper
“Generalized-ODIN (G-ODIN) (Hsu et al., 2020): a generalized OOD detection method that decomposes the confidence score and introduces a preprocessing layer that perturbs the inputs for better OOD detection.”
From QUTE: Quantifying Uncertainty in TinyML with Early-exit-assisted ensembles for model-monitoring (2024) · cites this paper as Hsu et al., 2020
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Densely Connected Convolutional Networks
Huang, G., Liu, Z., Van Der Maaten, L., & Weinberger, K. Q. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.243 · Cited by 48,566
Learning Multiple Layers of Features from Tiny Images
Krizhevsky, A. (2024) · doi:10.57702/zp44cu3g · Cited by 23,889
Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification
He, K., Zhang, X., Ren, S., & Sun, J. (2015) · 2015 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.123 · Cited by 19,741
Distilling the Knowledge in a Neural Network
Hinton, G., Vinyals, O., & Dean, J. (2015) · arXiv · doi:10.48550/arxiv.1503.02531 · Cited by 14,204
Identity mappings in deep residual networks
He, K., Zhang, X., Ren, S., & Sun, J. (2016) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-46493-0_38 · Cited by 10,741
Intriguing properties of neural networks
Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., & Fergus, R. (2013) · arXiv · doi:10.48550/arxiv.1312.6199 · Cited by 8,143
Zagoruyko, S., & Komodakis, N. (2016) · Procedings of the British Machine Vision Conference 2016 · doi:10.5244/c.30.87 · Cited by 7,244
Dropout as a bayesian approximation: Representing model uncertainty in deep learning
Gal, Y., & Ghahramani, Z. (2015) · arXiv · doi:10.48550/arxiv.1506.02142 · Cited by 6,471
Reading digits in natural images with unsupervised feature learning
Netzer, Y. (2024) · doi:10.57702/e7wwo0zk · Cited by 4,261
Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles
Lakshminarayanan, B., Pritzel, A., & Blundell, C. (2016) · arXiv · doi:10.48550/arxiv.1612.01474 · Cited by 3,666
Flagged first, then by citations
3 of the 45 references couldn’t be matched to a known paper.
Research works printed but not linked: 1
- shafaei, 0, A less biased evaluation of ood sample detectors, Proceedings of the British Machine Vision Conference (BMVC)
Cited by
Sorted by citations
Items 201–300 of 499
Choi, H., Yun, J. P., Kim, B. J., Jang, H., Shin, W., & Kim, S. W. (2024) · Journal of Industrial Information Integration · doi:10.1016/j.jii.2024.100559 · Cited by 6
RankFeat&RankWeight: Rank-1 Feature/Weight Removal for Out-of-Distribution Detection
Song, Y., Wang, W., & Sebe, N. (2024) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2024.3520899 · Cited by 29
Few-shot out-of-scope intent classification: analyzing the robustness of prompt-based learning
Jiang, Y., Raedt, M. D., Deleu, J., Demeester, T., & Develder, C. (2024) · Applied Intelligence · doi:10.1007/s10489-023-05215-x · Cited by 6
Exploration and Exploitation of Unlabeled Data for Open-Set Semi-supervised Learning
Zhao, G., Li, G., Qin, Y., Zhang, J., Chai, Z., Wei, X. K., Lin, L., & Yu, Y. (2024) · International Journal of Computer Vision · doi:10.1007/s11263-024-02155-y · Cited by 8
Improving Out-of-Distribution Generalization in Graphs via Hierarchical Semantic Environments
Piao, Y., Lee, S., Lu, Y., & Kim, S. (2024) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52733.2024.02609 · Cited by 6
Machine and deep learning performance in out-of-distribution regressions
Shmuel, A., Glickman, O., & Lazebnik, T. (2024) · Machine Learning Science and Technology · doi:10.1088/2632-2153/ada221 · Cited by 7
Can Your Generative Model Detect Out-of-Distribution Covariate Shift?
Viviers, C. G. A., Valiuddin, M. M. A., Caetano, F., Abdi, L., Filatova, L., de With, P. H. N., & van der Sommen, F. (2025) · Lecture notes in computer science · doi:10.1007/978-3-031-91585-7_12 · Cited by 6
Task-Agnostic Out-of-Distribution Detection Using Kernel Density Estimation
Erdil, E., Chaitanya, K., Karani, N., & Konukoğlu, E. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-87735-4_9 · Cited by 12
Effective Out-of-Distribution Detection in Classifier Based on PEDCC-Loss
Zhu, Q., Zheng, G., & Yan, Y. (2022) · Neural Processing Letters · doi:10.1007/s11063-022-10970-y · Cited by 7
Jiang, K., You, J., Dorj, U.-O., Kim, H., & Lee, J. (2022) · Frontiers in Plant Science · doi:10.3389/fpls.2022.989086 · Cited by 6
Open-World Lifelong Graph Learning
Hoffmann, M., Galke, L. P. A., & Ansgar Scherp, A. (2023) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn54540.2023.10191071 · Cited by 6
Identifying out of distribution samples for skin cancer and malaria images
Zaid, M., Ali, S., Ali, M., Hussein, S., Saadia, A., & Sultani, W. (2022) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2022.103882 · Cited by 8
Improving Energy-Based Out-of-Distribution Detection by Sparsity Regularization
Chen, Q., Jiang, W., Li, K., & Wang, Y. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-05936-0_42 · Cited by 6
Practical Evaluation of Out-of-Distribution Detection Methods for Image ClassificationPreprint
Techapanurak, E., & Okatani, T. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2101.02447 · Cited by 10
Zero-Shot Open Set Detection by Extending CLIPPreprint
Esmaeilpour, S., Liu, B., Robertson, E., & Shu, L. (2021) · arXiv (Cornell University) · Cited by 6
Semi-Supervised Object Detection with Uncurated Unlabeled Data for Remote Sensing ImagesPreprint
Liu, N., Xu, X., Gao, Y., Zhao, Y., & Li, H.-C. (2024) · SSRN Electronic Journal · doi:10.2139/ssrn.4684595 · Cited by 6
Characterizing Submanifold Region for Out-of-Distribution Detection
Li, X., Fang, Z., Zhang, Y., Ma, N., Bu, J., Han, B., & Wang, H. (2024) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2024.3468629 · Cited by 6
Enhancing Graph Neural Networks for Out-of-Distribution Graph Detection
Zhang, G., Yang, Z., Wu, J. X., Jiao, P., & Yang, J. Y. (2025) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2025.3584090 · Cited by 5
Percept, Memory, and Imagine: World Feature Simulating for Open-Domain Unknown Object Detection
Wu, A., & Deng, C. D. (2025) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52734.2025.00441 · Cited by 5
Feature Extraction for Out of Distribution Detection via Self-Supervised Learning
Thorp, C., Sisti, S., & Bennette, W. D. (2022) · International Conference on Machine Learning and Applications · doi:10.1109/icmla55696.2022.00153 · Cited by 5
Interpolation Normalization for Contrast Domain Generalization
Wang, M.-Z., Chen, J.-Y., Wang, H., Wu, H.-S., Liu, Z.-D., & Zhang, Q. (2023) · ACM Multimedia · doi:10.1145/3581783.3611841 · Cited by 5
WDiscOOD: Out-of-Distribution Detection via Whitened Linear Discriminant Analysis
Chen, Y., Lin, Y., Xu, R., & Vela, P. A. (2023) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.00488 · Cited by 9
Feng, S., Jin, P., & Wang, C. (2024) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v38i19.30100 · Cited by 5
Out-of-distribution Detection with Boundary Aware Learning
Pei, S., Zhang, X., Fan, B., & Meng, G. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-20053-3_14 · Cited by 5
Tolerate Failures of the Visual Camera With Robust Image Classifiers
Atif, M., Ceccarelli, A., Zoppi, T., & Bondavalli, A. (2023) · IEEE Access · doi:10.1109/access.2023.3237394 · Cited by 5
Implicit regularization of a deep augmented neural network model for human motion prediction
Yadav, G. K., Abdel‐Nasser, M., Rashwan, H. A., Puig, D., & Nandi, G. C. (2023) · Applied Intelligence · doi:10.1007/s10489-022-04419-x · Cited by 5
Intra-Class Mixup for Out-of-Distribution Detection
Ravikumar, D., Kodge, S., Garg, I., & Roy, K. (2023) · IEEE Access · doi:10.1109/access.2023.3254920 · Cited by 6
Lightweight Approaches to DNN Regression Error Reduction: An Uncertainty Alignment Perspective
Li, Z., Zhang, M., Xu, J., Yao, Y., Cao, C., Chen, T., Ma, X., & Lü, J. (2023) · ACM/IEEE International Conference on Software Engineering: Companion Proceedings (ICSE-Companion) · doi:10.1109/icse48619.2023.00106 · Cited by 5
Redesigning Out-of-Distribution Detection on 3D Medical Images
Vasiliuk, A., Frolova, D., Belyaev, M., & Shirokikh, B. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-44336-7_13 · Cited by 5
Enhancing the Power of OOD Detection via Sample-Aware Model Selection
Xue, F., He, Z. J., Zhang, Y., Xie, C., Li, Z., & Tan, F. (2024) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52733.2024.01623 · Cited by 5
Exploring feature sparsity for out-of-distribution detection
Chen, Q., Li, K., Chen, Z., Maul, T., & Yin, J. (2024) · Scientific Reports · doi:10.1038/s41598-024-79934-7 · Cited by 5
EARLIN: Early Out-of-Distribution Detection for Resource-Efficient Collaborative Inference
Nimi, S. T., Arefeen, M. A., Uddin, M. Y. S., & Lee, Y. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-86486-6_39 · Cited by 5
Araojo, T., Aresta, G., & Bogunović, H. (2022) · doi:10.1109/isbic56247.2022.9854763 · Cited by 5
Exploiting Mixed Unlabeled Data for Detecting Samples of Seen and Unseen Out-of-Distribution Classes
Sun, Y., & Wang, W. (2022) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v36i8.20814 · Cited by 5
Zhu, Q., Zheng, G., Shen, J., & Wang, R. (2022) · IEEE Access · doi:10.1109/access.2022.3184694 · Cited by 6
Semantic Driven Energy based Out-of-Distribution Detection
Joshi, A., Chalasani, S., & Iyer, K. N. (2022) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn55064.2022.9892318 · Cited by 5
Confidence-Calibrated Face Image Forgery Detection with Contrastive Representation Distillation
Yang, P., Huang, H., Wang, Z., Yu, A., & He, R. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-26316-3_1 · Cited by 6
Towards More Reliable Confidence Estimation
Qu, H., Foo, L. G., Li, Y., & Liu, J. (2023) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2023.3291676 · Cited by 5
Raising the Bar on the Evaluation of Out-of-Distribution Detection
Mukhoti, J., Lin, T., Chen, B.-C., Shah, A., Torr, P. H. S., Dokania, P. K., & Lim, S.-N. (2023) · IEEE International Conference on Computer Vision Workshops · doi:10.1109/iccvw60793.2023.00471 · Cited by 6
Resisting Out-of-Distribution Data Problem in Perturbation of XAIPreprint
Qiu, L., Yang, Y., Cao, C. C., Liu, J., Zheng, Y., Ngai, H. H. T., Hsiao, J. H., & Chen, L. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2107.14000 · Cited by 27
On Out-of-distribution Detection with Energy-based ModelsPreprint
Elflein, S., Charpentier, B., Zügner, D., & Günnemann, S. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2107.08785 · Cited by 12
Towards OOD Object Detection With Unknown-Concept Guided Feature Diffusion
Wu, A., & Deng, C. D. (2025) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2025.3590735 · Cited by 4
Enhanced Dual-Pattern Matching With Vision-Language Representation for Out-of-Distribution Detection
Xiang, X., Xu, Z., Zhang, Z., Zeng, Z., & Chen, X. (2025) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2025.3590717 · Cited by 4
Model-Free Test Time Adaptation for Out-of-Distribution Detection
Zhang, Y.-F., Wang, X., Zhou, T., Yuan, K., Zhang, Z., Wang, L., & Jin, R. (2025) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2025.3615192 · Cited by 9
Acoustic recognition of individuals in closed and open bird populations
Huang, L., Clarke, R. H., Teixeira, D., Chiaradia, A., & Meyer, B. E. (2025) · Ecological Informatics · doi:10.1016/j.ecoinf.2025.103330 · Cited by 8
Detecting Out-of-Distribution Examples Via Class-Conditional Impressions Reappearing
Chen, J., Qu, X., Li, J., Wang, J., Wan, J., & Xiao, J. J. (2023) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp49357.2023.10095909 · Cited by 4
Key Feature Replacement of In-Distribution Samples for Out-of-Distribution Detection
Kim, J., Kong, S. T., Na, D., & Jung, K.-H. (2023) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v37i7.25995 · Cited by 4
Alcover‐Couso, R., SanMiguel, J. C., & Escudero-Viñolo, M. (2023) · IEEE International Conference on Computer Vision Workshops · doi:10.1109/iccvw60793.2023.00492 · Cited by 4
Understanding the Dependence of Perception Model Competency on Regions in an Image
Pohland, S., & Tomlin, C. J. (2024) · Communications in computer and information science · doi:10.1007/978-3-031-63797-1_8 · Cited by 4
BayWatch: Leveraging Bayesian Neural Networks for Hardware Fault Tolerance and Monitoring
Hoefer, J., Stammler, M., Kreß, F., Hotfilter, T., Harbaum, T., & Becker, J. (2024) · Proceedings · doi:10.1109/dft63277.2024.10753546 · Cited by 4
Improving the Efficiency of Human-in-the-Loop Systems: Adding Artificial to Human Experts
Jakubik, J., Weber, D., Hemmer, P., Vössing, M., & Satzger, G. (2025) · Lecture notes in information systems and organisation · doi:10.1007/978-3-031-80122-8_9 · Cited by 8
Hyperbolic Metric Learning for Visual Outlier Detection
González-Jiménez, Á., Lionetti, S., Bazazian, D., Gottfrois, P., Gröger, F., Navarini, A. A., & Pouly, M. (2025) · Lecture notes in computer science · doi:10.1007/978-3-031-91585-7_20 · Cited by 4
Enhanced OoD Detection through Cross-Modal Alignment of Multi-Modal Representations
Kim, J., & Hwang, S. (2025) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52734.2025.02790 · Cited by 6
Unsupervised out-of-distribution detection using kernel density estimation
Erdil, E., Chaitanya, K., & Konukoğlu, E. (2020) · Cited by 4
Out-of-Distribution Detection Based on Distance Metric Learning
Yang, D., Shin, I., Ngoc, K. M., Kim, H., Yu, C., & Hwang, M. (2020) · International Conference on Smart Media and Applications (SMA) · doi:10.1145/3426020.3426076 · Cited by 4
Detecting Unknown DGAs without Context Information
Drichel, A., von Brandt, J., & Meyer, U. (2022) · International Conference on Availability, Reliability and Security (ARES) · doi:10.1145/3538969.3538990 · Cited by 4
A Robust Pedestrian Re-Identification and Out-Of-Distribution Detection Framework
Bouzid, A., Sierra-Sosa, D., & Elmaghraby, A. S. (2023) · Drones · doi:10.3390/drones7060352 · Cited by 4
Robust Uncertainty Estimation for Classification of Maritime Objects
Becktor, J., Schöller, F. E. T., Boukas, E., & Nalpantidis, L. (2023) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra48891.2023.10161452 · Cited by 4
Exploiting classifier inter-level features for efficient out-of-distribution detection
Fayyad, J., Gupta, K., Mahdian, N., Gruyer, D., & Najjaran, H. (2023) · Image and Vision Computing · doi:10.1016/j.imavis.2023.104897 · Cited by 4
Simple and Effective Out-of-Distribution Detection via Cosine-based Softmax Loss
Noh, S., Jeong, D., & Lee, J.-H. (2023) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.01518 · Cited by 4
CAME: Contrastive Automated Model Evaluation
Peng, R., Duan, Q., Wang, H., Ma, J., Jiang, Y., Tu, Y., Jiang, X., & Zhao, J. (2023) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.01841 · Cited by 6
Monitizer: Automating Design and Evaluation of Neural Network Monitors
Azeem, M., Grobelna, M., Kanav, S., Křetínský, J., Mohr, S., & Rieder, S. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-65630-9_14 · Cited by 4
Uncertainty-Guided Appearance-Motion Association Network for Out-of-Distribution Action Detection
Fang, X., Easwaran, A., & Genest, B. (2024) · Conference on Multimedia Information Processing and Retrieval · doi:10.1109/mipr62202.2024.00034 · Cited by 13
Label-Assisted Memory Autoencoder for Unsupervised Out-of-Distribution Detection
Zhang, S., Pan, C., Song, L., Wu, X., Hu, Z., Pei, K., Peter Tiňo, B., & Yao, X. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-86523-8_48 · Cited by 4
Symmetric Contrastive Loss for Out-of-Distribution Skin Lesion Detection
Li, X., Desrosiers, C., & Liu, X. (2022) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi52829.2022.9761434 · Cited by 5
Class-wise Thresholding for Robust Out-of-Distribution Detection
Guarrera, M., Jin, B., Lin, T.-W., Zuluaga, M. A., Chen, Y., & Sangiovanni-Vincentelli, A. L. (2022) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw56347.2022.00321 · Cited by 4
Macêdo, D., & Ludermir, T. B. (2021) · arXiv (Cornell University) · Cited by 4
Continual Novelty DetectionPreprint
Aljundi, R., Reino, D. O., Chumerin, N., & Turner, R. E. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2106.12964 · Cited by 5
Macêdo, D., & Ludermir, T. B. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2105.14399 · Cited by 17
Learning Visual-Semantic Hierarchical Attribute Space for Interpretable Open-Set Recognition
Xu, Z., & Xiang, X. (2025) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv61041.2025.00556 · Cited by 3
Towards More Trustworthy Deep Code Models by Enabling Out-of-Distribution Detection
Yan, Y., Duong, V., Shao, H., & Poshyvanyk, D. (2025) · ACM/IEEE International Conference on Software Engineering: Companion Proceedings (ICSE-Companion) · doi:10.1109/icse55347.2025.00177 · Cited by 3
Zhang, S., Dou, S., Li, Z., Yeung, K. L., & Quek, T. Q. S. (2025) · IEEE Transactions on Networking · doi:10.1109/ton.2025.3631083 · Cited by 3
BFAct: Out-of-Distribution Detection with Butterworth Filter Rectified Activations
Kong, H., & Li, H. (2023) · Communications in computer and information science · doi:10.1007/978-981-99-0617-8_9 · Cited by 3
Towards In-Distribution Compatible Out-of-Distribution Detection
Wu, B., Jiang, J., Ren, H., Du, Z., Wang, W., Li, Z., Cai, D., He, X., Lin, B., & Liu, W. (2023) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v37i9.26230 · Cited by 3
Baek, E., Park, K., Kim, J.-Y., & Kim, H. (2024) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52733.2024.02104 · Cited by 3
Enhancing Out-of-Distribution Detection through Dynamic Activation Function
Ji, Y., Zhu, Y. T., Li, Z., Chen, J., Kong, Y., & Chen, J. (2025) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp49660.2025.10890331 · Cited by 3
VisTa: Visual-contextual and Text-augmented Zero-shot Object-level OOD Detection
Zhang, B., Qu, X., Li, G., Wan, J., & Wang, J. (2025) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp49660.2025.10888875 · Cited by 3
EOOD: Entropy-based Out-of-distribution Detection
Yang, G., Hou, C., Peng, W., Fang, X., Nie, Y., Zhu, P., & Tang, K. (2025) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn64981.2025.11228668 · Cited by 5
Equipping Vision Foundation Model with Mixture of Experts for Out-of-Distribution Detection
Zhao, S., Liu, J., Wen, X., Tan, H., & Qi, X. (2025) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51701.2025.00171 · Cited by 3
Forward Model and Deep Learning Based Iterative Deconvolution for Robust Dynamic CT Perfusion
Sudarshan, V. P., Reddy, P. K., Gubbi, J., & Purushothaman, B. (2021) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc46164.2021.9630969 · Cited by 3
EDIR: Efficient Distributed Image Retrieval of Novel Objects in Mobile Networks
Felemban, N. M., Mehmeti, F., Porta, T. L., & Kwon, H. (2023) · IEEE Transactions on Mobile Computing · doi:10.1109/tmc.2023.3257268 · Cited by 3
Distribution Matching for Machine Teaching
Cao, X., & Tsang, I. W.-H. (2023) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2023.3256412 · Cited by 3
A Theoretical Analysis of Out-of-Distribution Detection in Multi-Label Classification
Zhang, D., & Taneva-Popova, B. (2023) · ACM SIGIR International Conference on Theory of Information Retrieval (ICTIR) · doi:10.1145/3578337.3605116 · Cited by 4
Are Deep Point Cloud Classifiers Suffer From Out-of-distribution Overconfidence Issue?
He, X., Tang, K., Shi, Y., Li, Y., Peng, W., & Zhu, P. (2023) · Conference proceedings/Conference proceedings - IEEE International Conference on Systems, Man, and Cybernetics · doi:10.1109/smc53992.2023.10394537 · Cited by 3
Revisiting Deep Ensemble for Out-of-Distribution Detection: A Loss Landscape Perspective
Fang, K., Tao, Q., Huang, X., & Yang, J. (2024) · International Journal of Computer Vision · doi:10.1007/s11263-024-02156-x · Cited by 6
Endoood: Uncertainty-Aware Out-of-Distribution Detection in Capsule Endoscopy Diagnosis
Tan, Q., Bai, L., Wang, G., Islam, M., & Ren, H. (2024) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi56570.2024.10635759 · Cited by 3
Multi-label Out-of-Distribution Detection with Spectral Normalized Joint Energy
Mei, Y., Wang, X., Zhang, D., & Wang, X. (2024) · Lecture notes in computer science · doi:10.1007/978-981-97-7244-5_3 · Cited by 7
Margin-Bounded Confidence Scores for Out-of-Distribution Detection
Tamang, L., Bouadjenek, M. R., Dazeley, R., & Aryal, S. (2024) · Proceedings · doi:10.1109/icdm59182.2024.00053 · Cited by 3
Hong, P., Quan, W., Chen, Z., Liu, X., & Gao, S. (2025) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2025.3554319 · Cited by 3
OAFN: An efficient open-world audio few-shot learning network for event classification
Chen, F., Xue, H., & Fang, P. (2025) · Knowledge-Based Systems · doi:10.1016/j.knosys.2025.114201 · Cited by 3
Anomaly detection methods in sample datasets when managing processes in systems by the state
Lykhach, O., Ugryumov, M., Shevchenko, D., & Шматков, С. И. (2022) · Bulletin of V N Karazin Kharkiv National University series «Mathematical modeling Information technology Automated control systems» · doi:10.26565/2304-6201-2022-53-03 · Cited by 3
Network Generalization Prediction for Safety Critical Tasks in Novel Operating Domains
O’Brien, M., Medoff, M., Bukowski, J. V., & Hager, G. (2022) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv51458.2022.00190 · Cited by 3
Methods for deep learning model failure detection and model adaption: A survey
Wu, X., Hu, Z., Pei, K., Song, L., Cao, Z. W., & Zhang, S. (2021) · IEEE International Symposium on Software Reliability Engineering Workshops (ISSREW) · doi:10.1109/issrew53611.2021.00066 · Cited by 3
Self-Assessment and Robust Anomaly Detection with Bayesian Deep Learning
Carannante, G., Dera, D., Aminul, O., Bouaynaya, N. C., & Rasool, G. (2022) · International Conference on Information Fusion (FUSION) · doi:10.23919/fusion49751.2022.9841358 · Cited by 4
Semantic Novelty Detection via Relational Reasoning
Borlino, F. C., Bucci, S., & Tommasi, T. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-19806-9_11 · Cited by 3
Episodic Projection Network for Out-of-Distribution Detection in Few-shot Learning
Fan, J., Ou, Z., Yu, X., Yang, J., Wang, S., Kang, X., Zhang, H., & Song, M. (2022) · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition · doi:10.1109/icpr56361.2022.9956184 · Cited by 3
The Compact Support Neural Network
Barbu, A., & Mou, H. (2021) · Sensors · doi:10.3390/s21248494 · Cited by 3
Statistical Testing for Efficient Out of Distribution Detection in Deep Neural Networks.Preprint
Haroush, M., Frostig, T., Heller, R., & Soudry, D. (2021) · arXiv (Cornell University) · Cited by 3
Enomoto, S., & Eda, T. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2104.09286 · Cited by 3
Shifting Transformation Learning for Out-of-Distribution DetectionPreprint
Mohseni, S., Vahdat, A., & Yadawa, J. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2106.03899 · Cited by 7
Metadata
- Authors
- Hsu, Y.-C., Shen, Y., Jin, H., & Kira, Z.
- Affiliations
- Georgia Institute of Technology (US)
- Samsung (United States) (US)
- Venue
- 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
- Publisher
- IEEE
- Published
- 2020-06
- Pages
- 10948-10957
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W3034230713
- Cited by
- 499
- References
- 45
- Citations by year
- 2026: 38 · 2025: 83 · 2024: 98 · 2023: 114 · 2022: 61 · 2021: 92 · 2020: 12
- FWCI
- 26.61
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Licence
- Locations
- IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Adversarial Robustness in Machine Learning
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Digital Media Forensic Detection
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Keywords
- out-of-distribution detection, Odin, confidence calibration, distribution shift, semantic change, image classification, anomaly detection
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
