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arXiv · 2209.11604

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

Yung-Chen Tang·Pin-Yu Chen·Tsung-Yi Ho
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Abstract

Neural network calibration is an essential task in deep learning to ensure consistency between the confidence of model prediction and the true correctness likelihood. In this paper, we propose a new post-processing calibration method called Neural Clamping, which employs a simple joint input-output transformation on a pre-trained classifier via a learnable universal input perturbation and an output temperature scaling parameter. Moreover, we provide theoretical explanations on why Neural Clamping is provably better than temperature scaling. Evaluated on BloodMNIST, CIFAR-100, and ImageNet image recognition datasets and a variety of deep neural network models, our empirical results show that Neural Clamping significantly outperforms state-of-the-art post-processing calibration methods. The code is available at github.com/yungchentang/NCToolkit, and the demo is available at huggingface.co/spaces/TrustSafeAI/NCTV.

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Yung-Chen Tang, Pin-Yu Chen, Tsung-Yi Ho. 2022-09-23. Neural Clamping: Joint Input Perturbation and Temperature Scaling for Neural Network Calibration. https://arxiv.org/abs/2209.11604

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