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Machine learning methods such as deep neural networks (DNNs), despite their success across different domains, are known to often generate incorrect predictions with high confidence on inputs outside their training distribution.
Specaugment: A simple data augmentation method for automatic speech recognition
Park, D. S.; Chan, W.; Zhang, Y.; Chiu, C.-C.; Zoph, B.; Cubuk, E. D.; and Le, Q. V. 2019 · 1904
Earlier work this paper cites.
Prediction and outlier detection in classification problems
Guan, L.; and Tibshirani, R. 2019 · 1905
Earlier work this paper cites.
Entropic Out-of-Distribution Detection
Macedo, D.; Ren, T. I.; Zanchettin, C.; Oliveira, A. L. I.; and Ludermir, T. 2021 · 1908
Earlier work this paper cites.
Towards neural networks that provably know when they don’t know
Meinke, A.; and Hein, M. 2019 · 1909
Earlier work this paper cites.
Triplet-center loss for multi-view 3d object retrieval
He, X.; Zhou, Y.; Zhou, Z.; Bai, S.; and Bai, X. 2018 · 1954
Earlier work this paper cites.
Nonparametrics: statistical methods based on ranks
Lehmann, E. L.; and D’Abrera, H. J. 1975 · 1975
Earlier work this paper cites.
Document image defect models
Baird, H. S. 1992 · 1992
Earlier work this paper cites.
Theory of Point Estimation
Lehmann, E.; and Casella, G. 1998 · 1998
Earlier work this paper cites.
Support vector method for novelty detection
Schölkopf, B.; Williamson, R. C.; Smola, A. J.; Shawe-Taylor, J.; Platt, J. C.; et al. 1999 · 1999
Earlier work this paper cites.
Classification-based anomaly detection for general data
Bergman, L.; and Hoshen, Y. 2020 · 2005
Earlier work this paper cites.
Algorithmic learning in a random world
Vovk, V.; Gammerman, A.; and Shafer, G. 2005 · 2005
Earlier work this paper cites.
Testing statistical hypotheses
Lehmann, E. L.; and Romano, J. P. 2006 · 2006
Earlier work this paper cites.
Adversarial Examples Detection and Analysis with Layer-wise Autoencoders
Wójcik, B.; Morawiecki, P.; Śmieja, M.; Krzyżek, T.; Spurek, P.; and Tabor, J. 2020 · 2006
Earlier work this paper cites.
A Tutorial on Conformal Prediction
Shafer, G.; and Vovk, V. 2008 · 2008
Earlier work this paper cites.
Imagenet: A large-scale hierarchical image database
Deng, J.; Dong, W.; Socher, R.; Li, L.-J.; Li, K.; and Fei-Fei, L. 2009 · 2009
Earlier work this paper cites.
Learning multiple layers of features from tiny images
Krizhevsky, A.; Hinton, G.; et al. 2009 · 2009
Earlier work this paper cites.
Prediction with confidence based on a random forest classifier
Devetyarov, D.; and Nouretdinov, I. 2010 · 2010
Earlier work this paper cites.
Energy-based Out-of-distribution Detection
Liu, W.; Wang, X.; Owens, J. D.; and Li, Y. 2020 · 2010
Earlier work this paper cites.
Sequential conformal anomaly detection in trajectories based on hausdorff distance
Laxhammar, R.; and Falkman, G. 2011 · 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. Y. 2011 · 2011
Earlier work this paper cites.
Plug-in martingales for testing exchangeability on-line
Fedorova, V.; Gammerman, A.; Nouretdinov, I.; and Vovk, V. 2012 · 2012
Earlier work this paper cites.
Imagenet classification with deep convolutional neural networks
Krizhevsky, A.; Sutskever, I.; and Hinton, G. E. 2012 · 2012
Earlier work this paper cites.
Conformal prediction for reliable machine learning: theory, adaptations and applications
Balasubramanian, V.; Ho, S.-S.; and Vovk, V. 2014 · 2014
Earlier work this paper cites.
Explaining and harnessing adversarial examples
Goodfellow, I. J.; Shlens, J.; and Szegedy, C. 2014 · 2014
Cited alongside, same era.
Deep speech: Scaling up end-to-end speech recognition
Hannun, A.; Case, C.; Casper, J.; Catanzaro, B.; Diamos, G.; Elsen, E.; Prenger, R.; Satheesh, S.; Sengupta, S.; Coates, A.; et al. 2014 · 2014
Cited alongside, same era.
Anomaly detection of trajectories with kernel density estimation by conformal prediction
Smith, J.; Nouretdinov, I.; Craddock, R.; Offer, C.; and Gammerman, A. 2014 · 2014
Cited alongside, same era.
Learning deep features for scene recognition using places database
Zhou, B.; Lapedriza, A.; Xiao, J.; Torralba, A.; and Oliva, A. 2014 · 2014
Cited alongside, same era.
Contextual action recognition with r* cnn
Gkioxari, G.; Girshick, R.; and Malik, J. 2015 · 2015
Cited alongside, same era.
To trust or not to trust a classifier
Jiang, H.; Kim, B.; Guan, M.; and Gupta, M. 2018 · 2018
Later among the works it cites.
A simple unified framework for detecting out-of-distribution samples and adversarial attacks
Lee, K.; Lee, K.; Lee, H.; and Shin, J. 2018 · 2018
Later among the works it cites.
Open category detection with PAC guarantees
Liu, S.; Garrepalli, R.; Dietterich, T.; Fern, A.; and Hendrycks, D. 2018 · 2018
Later among the works it cites.
Characterizing adversarial subspaces using local intrinsic dimensionality
Ma, X.; Li, B.; Wang, Y.; Erfani, S. M.; Wijewickrema, S.; Schoenebeck, G.; Song, D.; Houle, M. E.; and Bailey, J. 2018 · 2018
Later among the works it cites.
Deep k-nearest neighbors: Towards confident, interpretable and robust deep learning
Papernot, N.; and McDaniel, P. 2018 · 2018
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Lie groups, Lie algebras, and representations: an elementary introduction , volume 222
Hall, B. 2015 · 2015
Cited alongside, same era.
Inductive conformal anomaly detection for sequential detection of anomalous sub-trajectories
Laxhammar, R.; and Falkman, G. 2015 · 2015
Cited alongside, same era.
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 · 2015
Cited alongside, same era.
End to end learning for self-driving cars
Bojarski, M.; Del Testa, D.; Dworakowski, D.; Firner, B.; Flepp, B.; Goyal, P.; Jackel, L. D.; Monfort, M.; Muller, U.; Zhang, J.; et al. 2016 · 2016
Cited alongside, same era.
Group equivariant convolutional networks
Cohen, T.; and Welling, M. 2016 · 2016
Cited alongside, same era.
Deep residual learning for image recognition
He, K.; Zhang, X.; Ren, S.; and Sun, J. 2016 · 2016
Cited alongside, same era.
A baseline for detecting misclassified and out-of-distribution examples in neural networks
Hendrycks, D.; and Gimpel, K. 2016 · 2016
Cited alongside, same era.
Later among the works it cites.
Deep one-class classification
Ruff, L.; Vandermeulen, R.; Goernitz, N.; Deecke, L.; Siddiqui, S. A.; Binder, A.; Müller, E.; and Kloft, M. 2018 · 2018
Later among the works it cites.
Learning sound event classifiers from web audio with noisy labels
Fonseca, E.; Plakal, M.; Ellis, D. P.; Font, F.; Favory, X.; and Serra, X. 2019 · 2019
Later among the works it cites.
Deep anomaly detection with outlier exposure
Hendrycks, D.; Mazeika, M.; and Dietterich, T. 2019 · 2019
Later among the works it cites.
Using self-supervised learning can improve model robustness and uncertainty
Hendrycks, D.; Mazeika, M.; Kadavath, S.; and Song, D. 2019 · 2019
Later among the works it cites.
Avt: Unsupervised learning of transformation equivariant representations by autoencoding variational transformations
Qi, G.-J.; Zhang, L.; Chen, C. W.; and Tian, Q. 2019 · 2019
Later among the works it cites.
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
Later among the works it cites.
The odds are odd: A statistical test for detecting adversarial examples
Roth, K.; Kilcher, Y.; and Hofmann, T. 2019 · 2019
Later among the works it cites.
Real-time out-of-distribution detection in learning-enabled cyber-physical systems
Cai, F.; and Koutsoukos, X. 2020 · 2020
Later among the works it cites.
A group-theoretic framework for data augmentation
Chen, S.; Dobriban, E.; and Lee, J. H. 2020 · 2020
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Learning with Out-of-Distribution Data for Audio Classification
Iqbal, T.; Cao, Y.; Kong, Q.; Plumbley, M. D.; and Wang, W. 2020 · 2020
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Detecting out-of-distribution examples with gram matrices
Sastry, C. S.; and Oore, S. 2020 · 2020
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Tack, J.; Mo, S.; Jeong, J.; and Shin, J. 2020 · 2020
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Zisselman, E.; and Tamar, A. 2020 · 2020
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Testing for Outliers with Conformal p-values
Bates, S.; Candès, E.; Lei, L.; Romano, Y.; and Sesia, M. 2021 · 2021
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Learning Augmentation Distributions using Transformed Risk Minimization
Chatzipantazis, E.; Pertigkiozoglou, S.; Daniilidis, K.; and Dobriban, E. 2021 · 2021
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Kaur, R.; Jha, S.; Roy, A.; Sokolsky, O.; and Lee, I. 2021 · 2021
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Multiscale Score Matching for Out-of-Distribution Detection
Mahmood, A.; Oliva, J.; and Styner, M. A. 2021 · 2021
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