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When presented with Out-of-Distribution (OOD) examples, deep neural networks yield confident, incorrect predictions.
Detecting out-of-distribution inputs to deep generative models using a test for typicality
E. T. Nalisnick, A. Matsukawa, Y. W. Teh, and B. Lakshminarayanan · 1906
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Likelihood ratios for out-of-distribution detection
J. Ren, P. J. Liu, E. Fertig, J. Snoek, R. Poplin, M. A. DePristo, J. V. Dillon, and B. Lakshminarayanan · 1906
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Unsupervised out-of-distribution detection by maximum classifier discrepancy
Q. Yu and K. Aizawa · 1908
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SUN database: Large-scale scene recognition from abbey to zoo
J. Xiao, J. Hays, K. A. Ehinger, A. Oliva, and A. Torralba · 2010
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Reading digits in natural images with unsupervised feature learning
Y. Netzer, T. Wang, A. Coates, A. Bissacco, B. Wu, and A. Y. Ng · 2011
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Calibration of confidence measures in speech recognition
D. Yu, J. Li, and L. Deng · 2011
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Describing textures in the wild
M. Cimpoi, S. Maji, I. Kokkinos, S. Mohamed, and A. Vedaldi · 2014
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Deep neural networks are easily fooled: High confidence predictions for unrecognizable images
A. M. Nguyen, J. Yosinski, and J. Clune · 2015
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Imagenet large scale visual recognition challenge
O. Russakovsky, J. Deng, H. Su, J. Krause, S. Satheesh, S. Ma, Z. Huang, A. Karpathy, A. Khosla, M. Bernstein, A. C. Berg, and L. Fei-Fei · 2015
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LSUN: construction of a large-scale image dataset using deep learning with humans in the loop
F. Yu, Y. Zhang, S. Song, A. Seff, and J. Xiao · 2015
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Dropout As a Bayesian Approximation: Representing Model Uncertainty in Deep Learning
Y. Gal and Z. Ghahramani · 2016
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Image style transfer using convolutional neural networks
L. A. Gatys, A. S. Ecker, and M. Bethge · 2016
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Deep residual learning for image recognition
K. He, X. Zhang, S. Ren, and J. Sun · 2016
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A baseline for detecting misclassified and out-of-distribution examples in neural networks
D. Hendrycks and K. Gimpel · 2017
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Densely connected convolutional networks
G. Huang, Z. Liu, L. van der Maaten, and K. Q. Weinberger · 2017
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Fashion-mnist: a novel image dataset for benchmarking machine learning algorithms, 2017
H. Xiao, K. Rasul, and R. Vollgraf · 2017
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Deep learning for classical japanese literature, 2018
T. Clanuwat, M. Bober-Irizar, A. Kitamoto, A. Lamb, K. Yamamoto, and D. Ha · 2018
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Learning confidence for out-of-distribution detection in neural networks
Out-of-distribution detection using multiple semantic label representations
G. Shalev, Y. Adi, and J. Keshet · 2018
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Detecting out-of-distribution inputs in deep neural networks using an early-layer output
V. Abdelzad, K. Czarnecki, R. Salay, T. Denounden, S. Vernekar, and B. Phan · 2019
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A variational dirichlet framework for out-of-distribution detection, 2019
W. Chen, Y. Shen, W. Wang, and H. Jin · 2019
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Bayesian variational autoencoders for unsupervised out-of-distribution detection
E. Daxberger and J. M. Hernández-Lobato · 2019
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Deep anomaly detection with outlier exposure
D. Hendrycks, M. Mazeika, and T. G. Dietterich · 2019
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T. DeVries and G. W. Taylor · 2018
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Deep anomaly detection using geometric transformations
I. Golan and R. El-Yaniv · 2018
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Training confidence-calibrated classifiers for detecting out-of-distribution samples
K. Lee, H. Lee, K. Lee, and J. Shin · 2018
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A simple unified framework for detecting out-of-distribution samples and adversarial attacks
K. Lee, K. Lee, H. Lee, and J. Shin · 2018
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Enhancing the reliability of out-of-distribution image detection in neural networks
S. Liang, Y. Li, and R. Srikant · 2018
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Predictive uncertainty estimation via prior networks
A. Malinin and M. J. F. Gales · 2018
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Using self-supervised learning can improve model robustness and uncertainty
D. Hendrycks, M. Mazeika, S. Kadavath, and D. Song
Cited in the paper.
Y. Huang, S. Dai, T. Nguyen, R. G. Baraniuk, and A. Anandkumar · 2019
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Do deep generative models know what they don’t know?
E. T. Nalisnick, A. Matsukawa, Y. W. Teh, D. Görür, and B. Lakshminarayanan · 2019
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Detecting out-of-distribution samples using low-order deep features statistics, 2019
I. M. Quintanilha, R. de M. E. Filho, J. Lezama, M. Delbracio, and L. O. Nunes · 2019
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Input complexity and out-of-distribution detection with likelihood-based generative models
J. Serrà, D. Álvarez, V. Gómez, O. Slizovskaia, J. F. Núñez, and J. Luque · 2019
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Unsupervised out-of-distribution detection with batch normalization
J. Song, Y. Song, and S. Ermon · 2019
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Out-of-distribution detection in classifiers via generation
S. Vernekar, A. Gaurav, V. Abdelzad, T. Denouden, R. Salay, and K. Czarnecki · 2019
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