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This paper presents a principled approach for detecting out-of-distribution (OOD) samples in deep neural networks (DNN).
Probabilistic modeling of deep features for out-of-distribution and adversarial detection
Ahuja, N. A., Ndiour, I., Kalyanpur, T., and Tickoo, O. (2019) · 1909
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Relations between two sets of variates
Hotelling, H. (1936) · 1936
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Kernel pca and de-noising in feature spaces
Mika, S., Schölkopf, B., Smola, A., Müller, K.-R., Scholz, M., and Rätsch, G. (1999) · 1999
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Kernel principal component analysis
Schölkopf, B., Smola, A., and Müller, K.-R. (1999) · 1999
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Nonlinear dimensionality reduction by locally linear embedding
Roweis, S. T. and Saul, L. K. (2000) · 2000
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A global geometric framework for nonlinear dimensionality reduction
Tenenbaum, J. B., De Silva, V., and Langford, J. C. (2000) · 2000
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A User’s Guide to Principal Components
Jackson, J. (2003) · 2003
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A kernel view of the dimensionality reduction of manifolds
Ham, J., Lee, D. D., Mika, S., and Schölkopf, B. (2004) · 2004
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Pattern recognition and machine learning
Bishop, C. M. (2006) · 2006
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Learning multiple layers of features from tiny images
Krizhevsky, A., Hinton, G., et al. (2009) · 2009
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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) · 2011
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A tutorial on principal component analysis
Shlens, J. (2014) · 2014
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Tiny imagenet visual recognition challenge
Le, Y. and Yang, X. (2015) · 2015
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Lsun: Construction of a large-scale image dataset using deep learning with humans in the loop
Yu, F., Zhang, Y., Song, S., Seff, A., and Xiao, J. (2015) · 2015
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Zagoruyko, S. and Komodakis, N. (2016) · 2016
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A baseline for detecting misclassified and out-of-distribution examples in neural networks
Hendrycks, D. and Gimpel, K. (2017) · 2017
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What uncertainties do we need in bayesian deep learning for computer vision?
Kendall, A. and Gal, Y. (2017) · 2017
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Simple and scalable predictive uncertainty estimation using deep ensembles
Lakshminarayanan, B., Pritzel, A., and Blundell, C. (2017) · 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) · 2018
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Gal, Y. and Ghahramani, Z. (2016) · 2016
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He, K., Zhang, X., Ren, S., and Sun, J. (2016) · 2016
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Van den Oord, A., Kalchbrenner, N., Espeholt, L., Vinyals, O., Graves, A., et al. (2016) · 2016
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Enhancing the reliability of out-of-distribution image detection in neural networks
Liang, S., Li, Y., and Srikant, R. (2018) · 2018
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Deep anomaly detection with outlier exposure
Hendrycks, D., Mazeika, M., and Dietterich, T. (2019) · 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) · 2019
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