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By design, discriminatively trained neural network classifiers produce reliable predictions only for in-distribution samples.
Analysis of confident-classifiers for out-of-distribution detection
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Likelihood ratios for out-of-distribution detection
Ren, J., Liu, P. J., Fertig, E., Snoek, J., Poplin, R., DePristo, M. A., Dillon, J. V., and Lakshminarayanan, B. (2019) · 1906
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Algorithms for manifold learning
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Sample complexity of testing the manifold hypothesis
Narayanan, H. and Mitter, S. (2010) · 2010
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Notmnist dataset
Bulatov, Y. (2011) · 2011
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The manifold tangent classifier
Rifai, S., N, Y., Dauphin, Vincent, P., Bengio, Y., and Muller, X. (2011) · 2011
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Adadelta: an adaptive learning rate method
Zeiler, M. D. (2012) · 2012
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Auto-encoding variational bayes
Kingma, D. P. and Welling, M. (2013) · 2013
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Generative adversarial nets
Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., and Bengio, Y. (2014) · 2014
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Human-level concept learning through probabilistic program induction
Lake, B. M., Salakhutdinov, R., and Tenenbaum, J. B. (2015) · 2015
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Makhzani, A., Shlens, J., Jaitly, N., Goodfellow, I., and Frey, B. (2015) · 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) · 2015
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Dropout as a bayesian approximation: Representing model uncertainty in deep learning
Gal, Y. and Ghahramani, Z. (2016) · 2016
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A baseline for detecting misclassified and out-of-distribution examples in neural networks
Hendrycks, D. and Gimpel, K. (2016) · 2016
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Fashion-mnist: a novel image dataset for benchmarking machine learning algorithms
Xiao, H., Rasul, K., and Vollgraf, R. (2017) · 2017
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Learning confidence for out-of-distribution detection in neural networks
DeVries, T. and Taylor, G. W. (2018) · 2018
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Boosting uncertainty estimation for deep neural classifiers
Geifman, Y., Uziel, G., and El-Yaniv, R. (2018) · 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) · 2018
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Generative probabilistic novelty detection with adversarial autoencoders
Pidhorskyi, S., Almohsen, R., Adjeroh, D. A., and Doretto, G. (2018) · 2018
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Deep bayesian active learning with image data
Gal, Y., Islam, R., and Ghahramani, Z. (2017) · 2017
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On calibration of modern neural networks
Guo, C., Pleiss, G., Sun, Y., and Weinberger, K. Q. (2017) · 2017
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Safer classification by synthesis
Wang, W., Wang, A., Tamar, A., Chen, X., and Abbeel, P. (2017) · 2017
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Training confidence-calibrated classifiers for detecting out-of-distribution samples
Lee, K., Lee, H., Lee, K., and Shin, J. (2018a)
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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. (2018b)
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Sricharan, K. and Srivastava, A. (2018) · 2018
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Natural adversarial examples
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Why relu networks yield high-confidence predictions far away from the training data and how to mitigate the problem
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