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Whereas the ability of deep networks to produce useful predictions has been amply demonstrated, estimating the reliability of these predictions remains challenging.
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Rank Consistent Ordinal Regression for Neural Networks with Application to Age Estimation
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On Calibration of Modern Neural Networks
C. Guo, G. Pleiss, Y. Sun, and K. Q. Weinberger · 2017
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What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision?
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Simple and Scalable Predictive Uncertainty Estimation Using Deep Ensembles
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Geometric Deep Learning on Graphs and Manifolds Using Mixture Model CNNs
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An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
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Simple and Principled Uncertainty Estimation with Deterministic Deep Learning via Distance Awareness
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(probabilistic pixel-adaptive refinement networks)
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Cartoon Face Recognition: A Benchmark Dataset
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Deep Ordinal Regression with Label Diversity
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Natural adversarial examples
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Understanding Deep Learning (still) Requires Rethinking Generalization
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Gradient-based uncertainty for monocular depth estimation
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