Neural Clamping: Joint Input Perturbation and Temperature Scaling for Neural Network Calibration

Probabilistic outputs for support vector machines and comparisons to regularized likelihood methods

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Deep residual learning for image recognition

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Wide residual networks

Original

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Dermatologist-level classification of skin cancer with deep neural networks

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On calibration of modern neural networks

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Densely connected convolutional networks

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Focal loss for dense object detection

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Uncertainty in machine learning: A safety perspective on autonomous driving

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Beyond temperature scaling: Obtaining well-calibrated multi-class probabilities with dirichlet calibration

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Verified uncertainty calibration

Ananya Kumar, Percy S Liang, and Tengyu Ma · 2019

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When does label smoothing help?

Rafael Müller, Simon Kornblith, and Geoffrey E Hinton · 2019

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Measuring calibration in deep learning

Jeremy Nixon, Michael W Dusenberry, Linchuan Zhang, Ghassen Jerfel, and Dustin Tran · 2019

Calibrating deep neural networks using focal loss

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On focal loss for class-posterior probability estimation: A theoretical perspective

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Revisiting the calibration of modern neural networks

Matthias Minderer, Josip Djolonga, Rob Romijnders, Frances Hubis, Xiaohua Zhai, Neil Houlsby, Dustin Tran, and Mario Lucic · 2021

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Improving calibration through the relationship with adversarial robustness

Yao Qin, Xuezhi Wang, Alex Beutel, and Ed Chi · 2021

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A geometric perspective towards neural calibration via sensitivity decomposition

Junjiao Tian, Dylan Yung, Yen-Chang Hsu, and Zsolt Kira · 2021

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