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Despite having excellent performances for a wide variety of tasks, modern neural networks are unable to provide a reliable confidence value allowing to detect misclassifications.
The mnist dataset of handwritten digits. 1998
LeCun, Y., Cortes, C., and Burges, C · 1998
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The cifar-10 dataset
Krizhevsky, A., Nair, V., and Hinton, G · 2014
Earlier work this paper cites.
Intriguing properties of neural networks
Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., and Fergus, R · 2014
Earlier work this paper cites.
Explaining and harnessing adversarial examples
Goodfellow, I., Shlens, J., and Szegedy, C · 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
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Deep learning strong parts for pedestrian detection
Tian, Y., Luo, P., Wang, X., and Tang, X · 2015
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Concrete problems in ai safety
Amodei, D., Olah, C., Steinhardt, J., Christiano, P., Schulman, J., and Mané, D · 2016
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Deepfool: a simple and accurate method to fool deep neural networks
Moosavi-Dezfooli, S.-M., Fawzi, A., and Frossard, P · 2016
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Wide residual networks
Zagoruyko, S. and Komodakis, N · 2016
Cited alongside, same era.
On calibration of modern neural networks
Guo, C., Pleiss, G., Sun, Y., and Weinberger, K. Q · 2017
Cited alongside, same era.
A baseline for detecting misclassified and out-of-distribution examples in neural networks
Hendrycks, D. and Gimpel, K · 2017
Cited alongside, same era.
Enhancing the reliability of out-of-distribution image detection in neural networks
Liang, S., Li, Y., and Srikant, R · 2017
Deep learning in medical image analysis
Shen, D., Wu, G., and Suk, H.-I · 2017
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Fashion-mnist: a novel image dataset for benchmarking machine learning algorithms
Xiao, H., Rasul, K., and Vollgraf, R · 2017
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Learning confidence for out-of-distribution detection in neural networks
DeVries, T. and Taylor, G. W · 2018
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To trust or not to trust a classifier
Jiang, H., Kim, B., Guan, M., and Gupta, M · 2018
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Technical report on the cleverhans v2.1.0 adversarial examples library
Papernot, N., Faghri, F., Carlini, N., Goodfellow, I., Feinman, R., Kurakin, A., Xie, C., Sharma, Y., Brown, T., Roy, A., Matyasko, A., Behzadan, V., Hambardzumyan, K., Zhang, Z., Juang, Y.-L., Li, Z., Sheatsley, R., Garg, A., Uesato, J., Gierke, W., Dong, Y., Berthelot, D., Hendricks, P., Rauber, J., and Long, R · 2018
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Cited alongside, same era.
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Boundary attack++: Query-efficient decision-based adversarial attack
Chen, J. and Jordan, M. I · 2019
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