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We present a principled approach for detecting out-of-distribution (OOD) and adversarial samples in deep neural networks.
A global geometric framework for nonlinear dimensionality reduction
Tenenbaum, J. B., De Silva, V., and Langford, J. C. (2000) · 2000
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Pattern recognition and machine learning
Bishop, C. M. (2006) · 2006
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The relationship between precision-recall and roc curves
Davis, J. and Goadrich, M. (2006) · 2006
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Visualizing data using t-sne
Maaten, L. v. d. and Hinton, G. (2008) · 2008
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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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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) · 2011
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Imagenet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., and Hinton, G. E. (2012) · 2012
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Ucf101: A dataset of 101 human actions classes from videos in the wild
Soomro, K., Zamir, A. R., and Shah, M. (2012) · 2012
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Explaining and harnessing adversarial examples
Goodfellow, I. J., Shlens, J., and Szegedy, C. (2015) · 2015
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Very deep convolutional networks for large-scale image recognition
Simonyan, K. and Zisserman, A. (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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Dropout as a bayesian approximation: Representing model uncertainty in deep learning
Gal, Y. and Ghahramani, Z. (2016) · 2016
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Dynamic network surgery for efficient dnns
Guo, Y., Yao, A., and Chen, Y. (2016) · 2016
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Deep residual learning for image recognition
He, K., Zhang, X., Ren, S., and Sun, J. (2016) · 2016
Emnist: an extension of mnist to handwritten letters
Cohen, G., Afshar, S., Tapson, J., and van Schaik, A. (2017) · 2017
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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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Can spatiotemporal 3d cnns retrace the history of 2d cnns and imagenet?
Hara, K., Kataoka, H., and Satoh, Y. (2018) · 2018
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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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Quo vadis, action recognition? a new model and the kinetics dataset
Carreira, J. and Zisserman, A. (2017) · 2017
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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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Adversarial framing for image and video classification
Zając, M., Żołna, K., Rostamzadeh, N., and Pinheiro, P. (2019) · 2019
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