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Out-of-distribution (OOD) detection is a critical task for deploying machine learning models in the open world.
On the generalized distance in statistics
Mahalanobis, P. C · 1936
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Robust estimation of a location parameter
Huber, P. J · 1964
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A class of invariant consistent tests for multivariate normality
Henze, N. and Zirkler, B · 1990
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Lof: identifying density-based local outliers
Breunig, M. M., Kriegel, H.-P., Ng, R. T., and Sander, J · 2000
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Estimating the Support of a High-Dimensional Distribution
Schölkopf, B., Platt, J. C., Shawe-Taylor, J., Smola, A. J., and Williamson, R. C · 2001
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A novel anomaly detection scheme based on principal component classifier
Shyu, M.-L., Chen, S.-C., Sarinnapakorn, K., and Chang, L · 2003
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Isolation forest
Liu, F. T., Ting, K. M., and Zhou, Z.-H · 2008
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Imagenet: A large-scale hierarchical image database
Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., and Fei-Fei, L · 2009
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Sun database: Large-scale scene recognition from abbey to zoo
Xiao, J., Hays, J., Ehinger, K. A., Oliva, A., and Torralba, A · 2010
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Reading digits in natural images with unsupervised feature learning
Netzer, Y., Wang, T., Coates, A., Bissacco, A., Wu, B., and Ng, A. Y · 2011
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Describing textures in the wild
Cimpoi, M., Maji, S., Kokkinos, I., Mohamed, S., and Vedaldi, A · 2014
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Anomaly detection using self-organizing maps-based k-nearest neighbor algorithm
Jing, T., Michael, A., and Pech, M · 2014
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Towards open world recognition
Bendale, A. and Boult, T · 2015
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Distance-based k-nearest neighbors outlier detection method in large-scale traffic data
Dang, T. T., Ngan, H. Y., and Liu, W · 2015
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Deep neural networks are easily fooled: High confidence predictions for unrecognizable images
Nguyen, A., Yosinski, J., and Clune, J · 2015
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Turkergaze: Crowdsourcing saliency with webcam based eye tracking
Xu, P., Ehinger, K. A., Zhang, Y., Finkelstein, A., Kulkarni, S. R., and Xiao, J · 2015
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Lsun: Construction of a large-scale image dataset using deep learning with humans in the loop
Yu, F., Seff, A., Zhang, Y., Song, S., Funkhouser, T., and Xiao, J · 2015
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Identity mappings in deep residual networks
He, K., Zhang, X., Ren, S., and Sun, J · 2016
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Stochastic gradient descent with warm restarts
Loshchilov, I. and Hutter, F · 2016
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Loda: Lightweight on-line detector of anomalies
Pevnỳ, T · 2016
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A baseline for detecting misclassified and out-of-distribution examples in neural networks
Hendrycks, D. and Gimpel, K · 2017
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Densely connected convolutional networks
Huang, G., Liu, Z., Van Der Maaten, L., and Weinberger, K. Q · 2017
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Simple and scalable predictive uncertainty estimation using deep ensembles
Lakshminarayanan, B., Pritzel, A., and Blundell, C · 2017
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Training confidence-calibrated classifiers for detecting out-of-distribution samples
Lee, K., Lee, H., Lee, K., and Shin, J · 2017
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Places: A 10 million image database for scene recognition
Zhou, B., Lapedriza, A., Khosla, A., Oliva, A., and Torralba, A · 2017
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Discriminative out-of-distribution detection for semantic segmentation
Bevandić, P., Krešo, I., Oršić, M., and Šegvić, S · 2018
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Supervised contrastive learning
Khosla, P., Teterwak, P., Wang, C., Sarna, A., Tian, Y., Isola, P., Maschinot, A., Liu, C., and Krishnan, D · 2020
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Energy-based out-of-distribution detection
Liu, W., Wang, X., Owens, J., and Li, Y · 2020
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Self-supervised learning for generalizable out-of-distribution detection
Mohseni, S., Pitale, M., Yadawa, J., and Wang, Z · 2020
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Towards knowledge uncertainty estimation for open set recognition
Pires, C., Barandas, M., Fernandes, L., Folgado, D., and Gamboa, H · 2020
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Csi: Novelty detection via contrastive learning on distributionally shifted instances
Tack, J., Mo, S., Jeong, J., and Shin, J · 2020
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Uncertainty estimation using a single deep deterministic neural network
Van Amersfoort, J., Smith, L., Teh, Y. W., and Gal, Y · 2020
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A simple unified framework for detecting out-of-distribution samples and adversarial attacks
Lee, K., Lee, K., Lee, H., and Shin, J · 2018
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Enhancing the reliability of out-of-distribution image detection in neural networks
Liang, S., Li, Y., and Srikant, R · 2018
Cited alongside, same era.
Predictive uncertainty estimation via prior networks
Malinin, A. and Gales, M · 2018
Cited alongside, same era.
Umap: Uniform manifold approximation and projection
McInnes, L., Healy, J., Saul, N., and Grossberger, L · 2018
Cited alongside, same era.
The inaturalist species classification and detection dataset
Van Horn, G., Mac Aodha, O., Song, Y., Cui, Y., Sun, C., Shepard, A., Adam, H., Perona, P., and Belongie, S · 2018
Cited alongside, same era.
Selectivenet: A deep neural network with an integrated reject option
Geifman, Y. and El-Yaniv, R · 2019
Cited alongside, same era.
Statistical analysis of nearest neighbor methods for anomaly detection
Gu, X., Akoglu, L., and Rinaldo, A · 2019
Cited alongside, same era.
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Contrastive training for improved out-of-distribution detection
Winkens, J., Bunel, R., Roy, A. G., Stanforth, R., Natarajan, V., Ledsam, J. R., MacWilliams, P., Kohli, P., Karthikesalingam, A., Kohl, S., et al · 2020
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Analysis of knn density estimation
Zhao, P. and Lai, L · 2020
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Atom: Robustifying out-of-distribution detection using outlier mining
Chen, J., Li, Y., Wu, X., Liang, Y., and Jha, S · 2021
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An image is worth 16x16 words: Transformers for image recognition at scale
Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., Uszkoreit, J., and Houlsby, N · 2021
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Mos: Towards scaling out-of-distribution detection for large semantic space
Huang, R. and Li, Y · 2021
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On the importance of gradients for detecting distributional shifts in the wild
Huang, R., Geng, A., and Li, Y · 2021
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Mood: Multi-level out-of-distribution detection
Lin, Z., Roy, S. D., and Li, Y · 2021
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Ssd: A unified framework for self-supervised outlier detection
Sehwag, V., Chiang, M., and Mittal, P · 2021
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React: Out-of-distribution detection with rectified activations
Sun, Y., Guo, C., and Li, Y · 2021
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Can multi-label classification networks know what they don’t know?
Wang, H., Liu, W., Bocchieri, A., and Li, Y · 2021
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Semantically coherent out-of-distribution detection
Yang, J., Wang, H., Feng, L., Yan, X., Zheng, H., Zhang, W., and Liu, Z · 2021
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Training ood detectors in their natural habitats
Katz-Samuels, J., Nakhleh, J., Nowak, R., and Li, Y · 2022
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Provable guarantees for understanding out-of-distribution detection
Morteza, P. and Li, Y · 2022
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Vim: Out-of-distribution with virtual-logit matching
Wang, H., Li, Z., Feng, L., and Zhang, W · 2022
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Mitigating neural network overconfidence with logit normalization
Wei, H., Xie, R., Cheng, H., Feng, L., An, B., and Li, Y · 2022
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