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arXiv · 2302.06245

Calibrating a Deep Neural Network with Its Predecessors

Linwei Tao·Minjing Dong·Daochang Liu·Changming Sun·Chang Xu
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Abstract

Confidence calibration - the process to calibrate the output probability distribution of neural networks - is essential for safety-critical applications of such networks. Recent works verify the link between mis-calibration and overfitting. However, early stopping, as a well-known technique to mitigate overfitting, fails to calibrate networks. In this work, we study the limitions of early stopping and comprehensively analyze the overfitting problem of a network considering each individual block. We then propose a novel regularization method, predecessor combination search (PCS), to improve calibration by searching a combination of best-fitting block predecessors, where block predecessors are the corresponding network blocks with weight parameters from earlier training stages. PCS achieves the state-of-the-art calibration performance on multiple datasets and architectures. In addition, PCS improves model robustness under dataset distribution shift.

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Linwei Tao, Minjing Dong, Daochang Liu, Changming Sun, Chang Xu. 2023-02-13. Calibrating a Deep Neural Network with Its Predecessors. https://arxiv.org/abs/2302.06245

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