Domain-adaptive and Subgroup-specific Cascaded Temperature Regression for Out-of-distribution Calibration

• URL: http://arxiv.org/abs/2402.09204v1
• Date: Wed, 14 Feb 2024 14:35:57 GMT
• Title: Domain-adaptive and Subgroup-specific Cascaded Temperature Regression for Out-of-distribution Calibration
• Authors: Jiexin Wang, Jiahao Chen, Bing Su
• Abstract summary: We propose a novel meta-set-based cascaded temperature regression method for post-hoc calibration. We partition each meta-set into subgroups based on predicted category and confidence level, capturing diverse uncertainties. A regression network is then trained to derive category-specific and confidence-level-specific scaling, achieving calibration across meta-sets.
• Score: 16.930766717110053
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Although deep neural networks yield high classification accuracy given sufficient training data, their predictions are typically overconfident or under-confident, i.e., the prediction confidences cannot truly reflect the accuracy. Post-hoc calibration tackles this problem by calibrating the prediction confidences without re-training the classification model. However, current approaches assume congruence between test and validation data distributions, limiting their applicability to out-of-distribution scenarios. To this end, we propose a novel meta-set-based cascaded temperature regression method for post-hoc calibration. Our method tailors fine-grained scaling functions to distinct test sets by simulating various domain shifts through data augmentation on the validation set. We partition each meta-set into subgroups based on predicted category and confidence level, capturing diverse uncertainties. A regression network is then trained to derive category-specific and confidence-level-specific scaling, achieving calibration across meta-sets. Extensive experimental results on MNIST, CIFAR-10, and TinyImageNet demonstrate the effectiveness of the proposed method.

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