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Typical Bayesian approaches to OOD detection use epistemic uncertainty.
Classification with reject option
Herbei, R. and Wegkamp, M. H · 2006
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Classification with a reject option using a hinge loss
Bartlett, P. L. and Wegkamp, M. 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
Earlier work this paper cites.
Aleatory or epistemic? does it matter?
Der Kiureghian, A. and Ditlevsen, O · 2009
Earlier work this paper cites.
Learning multiple layers of features from tiny images
Krizhevsky, A., Hinton, G., et al · 2009
Earlier work this paper cites.
Distinguishing two dimensions of uncertainty
Fox, C. R. and Ülkümen, G · 2011
Earlier work this paper cites.
Reading digits in natural images with unsupervised feature learning
Netzer, Y., Wang, T., Coates, A., Bissacco, A., Wu, B., and Ng, A · 2011
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Scikit-learn: Machine learning in Python
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., and Duchesnay, E · 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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Adam: A method for stochastic optimization
Kingma, D. P. and Ba, J · 2015
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Learning with rejection
Cortes, C., DeSalvo, G., and Mohri, M · 2016
Earlier work this paper cites.
A baseline for detecting misclassified and out-of-distribution examples in neural networks
Hendrycks, D. and Gimpel, K · 2016
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Pixel recurrent neural networks
Oord, A., Kalchbrenner, N., and Kavukcuoglu, K · 2016
Earlier work this paper cites.
Wide residual networks
Zagoruyko, S. and Komodakis, N · 2016
Earlier work this paper cites.
What uncertainties do we need in bayesian deep learning for computer vision?
Kendall, A. and Gal, Y · 2017
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Krueger, D., Huang, C., Islam, R., Turner, R., Lacoste, A., and Courville, A. C · 2017
Earlier work this paper cites.
Simple and scalable predictive uncertainty estimation using deep ensembles
Lakshminarayanan, B., Pritzel, A., and Blundell, C · 2017
Cited alongside, same era.
Training confidence-calibrated classifiers for detecting out-of-distribution samples
Lee, K., Lee, H., Lee, K., and Shin, J · 2017
Cited alongside, same era.
Principled detection of out-of-distribution examples in neural networks
Liang, S., Li, Y., and Srikant, R · 2017
Cited alongside, same era.
Multiplicative normalizing flows for variational bayesian neural networks
Louizos, C. and Welling, M · 2017
Cited alongside, same era.
Implicit weight uncertainty in neural networks
Pawlowski, N., Rajchl, M., and Glocker, B · 2017
Cited alongside, same era.
Does your model know the digit 6 is not a cat? a less biased evaluation of" outlier" detectors
Shafaei, A., Schmidt, M., and Little, J. J · 2018
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A simple baseline for bayesian uncertainty in deep learning
Maddox, W. J., Izmailov, P., Garipov, T., Vetrov, D. P., and Wilson, A. G · 2019
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Can you trust your model’s uncertainty? evaluating predictive uncertainty under dataset shift
Ovadia, Y., Fertig, E., Ren, J., Nado, Z., Sculley, D., Nowozin, S., Dillon, J., Lakshminarayanan, B., and Snoek, J · 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. A., Dillon, J. V., and Lakshminarayanan, B · 2019
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Out-of-distribution detection in classifiers via generation
Vernekar, S., Gaurav, A., Abdelzad, V., Denouden, T., Salay, R., and Czarnecki, K · 2019
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Safer classification by synthesis
Wang, W., Wang, A., Tamar, A., Chen, X., and Abbeel, P · 2017
Cited alongside, same era.
Waic, but why? generative ensembles for robust anomaly detection
Choi, H., Jang, E., and Alemi, A. A · 2018
Cited alongside, same era.
Decomposition of uncertainty in bayesian deep learning for efficient and risk-sensitive learning
Depeweg, S., Hernandez-Lobato, J.-M., Doshi-Velez, F., and Udluft, S · 2018
Cited alongside, same era.
Learning confidence for out-of-distribution detection in neural networks
DeVries, T. and Taylor, G. W · 2018
Cited alongside, same era.
Reducing network agnostophobia
Dhamija, A. R., Günther, M., and Boult, T. E · 2018
Cited alongside, same era.
Deep anomaly detection with outlier exposure
Hendrycks, D., Mazeika, M., and Dietterich, T · 2018
Cited alongside, same era.
Approximating the predictive distribution via adversarially-trained hypernetworks
Henning, C., von Oswald, J., Sacramento, J., Surace, S. C., Pfister, J.-P., and Grewe, B. F · 2018
Cited alongside, same era.
Later among the works it cites.
Batchensemble: an alternative approach to efficient ensemble and lifelong learning
Wen, Y., Tran, D., and Ba, J · 2019
Later among the works it cites.
A statistical theory of semi-supervised learning
Aitchison, L · 2020
Later among the works it cites.
Bacoun: Bayesian classifers with out-of-distribution uncertainty
Guénais, T., Vamvourellis, D., Yacoby, Y., Doshi-Velez, F., and Pan, W · 2020
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Ensemble distribution distillation
Malinin, A., Mlodozeniec, B., and Gales, M · 2020
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Density of states estimation for out-of-distribution detection
Morningstar, W. R., Ham, C., Gallagher, A. G., Lakshminarayanan, B., Alemi, A. A., and Dillon, J. V · 2020
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Consistent estimators for learning to defer to an expert
Mozannar, H. and Sontag, D · 2020
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The hidden uncertainty in a neural networks activations
Postels, J., Blum, H., Strümpler, Y., Cadena, C., Siegwart, R., Van Gool, L., and Tombari, F · 2020
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Cyclical stochastic gradient mcmc for bayesian deep learning
Zhang, R., Li, C., Zhang, J., Chen, C., and Wilson, A. G · 2020
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A statistical theory of cold posteriors in deep neural networks
Aitchison, L · 2021
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Bayesian neural network priors revisited
Fortuin, V., Garriga-Alonso, A., Wenzel, F., Rätsch, G., Turner, R., van der Wilk, M., and Aitchison, L · 2021
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What are bayesian neural network posteriors really like?
Izmailov, P., Vikram, S., Hoffman, M. D., and Wilson, A. G · 2021
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