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We study simple methods for out-of-distribution (OOD) image detection that are compatible with any already trained classifier, relying on only its predictions or learned representations.
Fast outlier detection in high dimensional spaces
Angiulli, F. and Pizzuti, C · 2002
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Learning multiple layers of features from tiny images
Krizhevsky, A · 2009
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The mnist database of handwritten digit images for machine learning research
Deng, L · 2012
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Deep residual learning for image recognition
He, K., Zhang, X., Ren, S., and Sun, J · 2016
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Ann-benchmarks: A benchmarking tool for approximate nearest neighbor algorithms
Aumüller, M., Bernhardsson, E., and Faithfull, A · 2017
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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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Fashion-mnist: a novel image dataset for benchmarking machine learning algorithms
Xiao, H., Rasul, K., and Vollgraf, R · 2017
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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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Do deep generative models know what they don’t know?
Nalisnick, E., Matsukawa, A., Teh, Y. W., Gorur, D., and Lakshminarayanan, B · 2018
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Deep anomaly detection with outlier exposure
Hendrycks, D., Mazeika, M., and Dietterich, T · 2019
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Likelihood ratios for out-of-distribution detection
Ren, J., Liu, P. J., Fertig, E., Snoek, J., Poplin, R., Depristo, M., Dillon, J., and Lakshminarayanan, B · 2019
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Deep nearest neighbor anomaly detection
Bergman, L., Cohen, N., and Hoshen, Y · 2020
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Beyer, L., Hénaff, O. J., Kolesnikov, A., Zhai, X., and Oord, A. v. d · 2020
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A simple framework for contrastive learning of visual representations
Chen, T., Kornblith, S., Norouzi, M., and Hinton, G · 2020
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AutoGluon-Tabular: Robust and accurate AutoML for structured data
Erickson, N., Mueller, J., Shirkov, A., Zhang, H., Larroy, P., Li, M., and Smola, A · 2020
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Swin transformer: Hierarchical vision transformer using shifted windows
Liu, Z., Lin, Y., Cao, Y., Hu, H., Wei, Y., Zhang, Z., Lin, S., and Guo, B · 2021
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Out-of-distribution detection for deep neural networks with isolation forest and local outlier factor
Luan, S., Gu, Z., Freidovich, L. B., Jiang, L., and Zhao, Q · 2021
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Data-centric AI competition
Ng, A · 2021
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A simple fix to Mahalanobis distance for improving near-OOD detection
Ren, J., Fort, S., Liu, J., Roy, A. G., Padhy, S., and Lakshminarayanan, B · 2021
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Generalized out-of-distribution detection: A survey
Yang, J., Zhou, K., Li, Y., and Liu, Z · 2021
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Maximizing overall diversity for improved uncertainty estimates in deep ensembles
Jain, S., Liu, G., Mueller, J., and Gifford, D · 2020
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Analysis of KNN density estimation
Zhao, P. and Lai, L · 2020
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Exploring the limits of out-of-distribution detection
Fort, S., Ren, J., and Lakshminarayanan, B · 2021
Cited alongside, same era.
On pitfalls in ood detection: Entropy considered harmful
Kirsch, A., Mukhoti, J., van Amersfoort, J., Torr, P. H. S., and Gal, Y · 2021
Cited alongside, same era.
Understanding failures in out-of-distribution detection with deep generative models
Zhang, L., Goldstein, M., and Ranganath, R · 2021
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Robust out-of-distribution detection for neural networks
Chen, J., Li, Y., Wu, X., Liang, Y., and Jha, S · 2022
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Out of distribution data detection in deployed machine learning models
Ghosh, P · 2022
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Open-set recognition: A good closed-set classifier is all you need?
Sagar, V., Han, K., Vedaldi, A., and Zisserman, A · 2022
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