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Discriminatively trained neural classifiers can be trusted, only when the input data comes from the training distribution (in-distribution).
Towards open set recognition
W. J. Scheirer, A. Rocha, A. Sapkota, and T. E. Boult · 2012
Earlier work this paper cites.
Generative adversarial nets
I. J. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, S. Ozair D. Warde-Farley, A. Courville, and Y. Bengio · 2014
Earlier work this paper cites.
Deep neural networks are easily fooled: High confidence predictions for unrecognizable images
Anh Nguyen, Jason Yosinski, and Jeff Clune · 2015
Earlier work this paper cites.
Safer classification by synthesis
W. Wang, A. Wang, A. Tamar, X. Chen, and P. Abbeel · 2017
Earlier work this paper cites.
A randomized gradient-free attack on relu networks
Francesco Croce and Matthias Hein · 2018
Cited alongside, same era.
Improving reconstruction autoencoder out-of-distribution detection with mahalanobis distance
Taylor Denouden, Rick Salay, Krzysztof Czarnecki, Vahdat Abdelzad, Buu Phan, and Sachin Vernekar · 2018
Cited alongside, same era.
Generative probabilistic novelty detection with adversarial autoencoders
S. Pidhorskyi, R. Almohsen, D. A. Adjeroh, and G. Doretto · 2018
Cited alongside, same era.
Building robust classifiers through generation of confident out of distribution examples
K. Sricharan and A. Srivastava · 2018
Cited alongside, same era.
Training confidence-calibrated classifiers for detecting out-of-distribution samples
Kimin Lee, Honglak Lee, Kibok Lee, and Jinwoo Shin
Cited in the paper.
A simple unified framework for detecting out-of-distribution samples and adversarial attacks
Kimin Lee, Kibok Lee, Honglak Lee, and Jinwoo Shin
Cited in the paper.
M. Hein, M. Andriushchenko, and J. Bitterwolf · 2019
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Deep anomaly detection with outlier exposure
Dan Hendrycks, Mantas Mazeika, and Thomas Dietterich · 2019
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Denoising autoencoders for overgeneralization in neural networks
G. Spigler · 2019
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