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In this work, we propose a method to reject out-of-distribution samples which can be adapted to any network architecture and requires no additional training data.
On the generalized distance in statistics
P.C. Mahalanobis · 1936
Earlier work this paper cites.
Decaf: A deep convolutional activation feature for generic visual recognition
J. Donahue, Y. Jia, O. Vinyals, J. Hoffman, N. Zhang, E. Tzeng, and T. Darrell · 2013
Earlier work this paper cites.
Deep residual learning for image recognition
K. He, X. Zhang, S. Ren, and J. Sun · 2016
Earlier work this paper cites.
A baseline for detecting misclassified and out-of-distribution examples in neural networks
D. Hendrycks and K. Gimpel · 2017
Cited alongside, same era.
Opening the black box of deep neural networks via information
R. Shwartz-Ziv and N. Tishby · 2017
Cited alongside, same era.
Chestx-ray8: Hospital-scale chest x-ray database and benchmarks on weakly-supervised classification and localization of common thorax diseases
X. Wang, Y. Peng, L. Lu, Z. Lu, M. Bagheri, and R.M. Summers · 2017
Cited alongside, same era.
Improving reconstruction autoencoder out-of-distribution detection with mahalanobis distance
T. Denouden, R. Salay, K. Czarnecki, V. Abdelzad, B. Phan, and S. Vernekar · 2018
Later among the works it cites.
A simple unified framework for detecting out-of-distribution samples and adversarial attacks
K. Lee, K. Lee, H. Lee, and J. Shin · 2018
Later among the works it cites.
Handling label noise through model confidence and uncertainty: application to chest radiograph classification
E. Çallı, E. Sogancioglu, E.Th. Scholten, K. Murphy, and B. van Ginneken · 2019
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