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Out-of-distribution detection is an important component of reliable ML systems.
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Krizhevsky, A · 2009
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Selfaugment: Automatic augmentation policies for self-supervised learning
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A unifying review of deep and shallow anomaly detection
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Reading digits in natural images with unsupervised feature learning
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Imagenet classification with deep convolutional neural networks
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Deep learning face attributes in the wild
Liu, Z., Luo, P., Wang, X., and Tang, X · 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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Very deep convolutional networks for large-scale image recognition
Simonyan, K. and Zisserman, A · 2015
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Yu, F., Zhang, Y., Song, S., Seff, A., and Xiao, J · 2015
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Amodei, D., Olah, C., Steinhardt, J., Christiano, P., Schulman, J., and Mané, D · 2016
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Deep anomaly detection with outlier exposure
Hendrycks, D., Mazeika, M., and Dietterich, T · 2019
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Detecting semantic anomalies
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A simple framework for contrastive learning of visual representations
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Energy-based out-of-distribution detection
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Consistent estimators for learning to defer to an expert
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Deep learning and computer vision will transform entomology
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