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

arXiv: https://arxiv.org/abs/2402.09204 · HTML: https://mlsanity.com/paper/2402.09204/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 22 references that are in the map.
- First posted on arXiv 14 Feb 2024.

## Builds on (most cited first, 17 of 17)

- [On Calibration of Modern Neural Networks](https://mlsanity.com/paper/1706.04599/) (2017)
- [Enhancing The Reliability of Out-of-distribution Image Detection in Neural Networks](https://mlsanity.com/paper/1706.02690/) (2017)
- [Deep Neural Networks are Easily Fooled: High Confidence Predictions for Unrecognizable Images](https://mlsanity.com/paper/1412.1897/) (2014)
- [Measuring Calibration in Deep Learning](https://mlsanity.com/paper/1904.01685/) (2019)
- [Deep k-Nearest Neighbors: Towards Confident, Interpretable and Robust Deep Learning](https://mlsanity.com/paper/1803.04765/) (2018)
- [To Trust Or Not To Trust A Classifier](https://mlsanity.com/paper/1805.11783/) (2018)
- [Mix-n-Match: Ensemble and Compositional Methods for Uncertainty Calibration in Deep Learning](https://mlsanity.com/paper/2003.07329/) (2020)
- [Are Labels Always Necessary for Classifier Accuracy Evaluation?](https://mlsanity.com/paper/2007.02915/) (2020)
- [The Devil is in the Margin: Margin-based Label Smoothing for Network Calibration](https://mlsanity.com/paper/2111.15430/) (2021)
- [What Does Rotation Prediction Tell Us about Classifier Accuracy under Varying Testing Environments?](https://mlsanity.com/paper/2106.05961/) (2021)
- [Post-hoc Uncertainty Calibration for Domain Drift Scenarios](https://mlsanity.com/paper/2012.10988/) (2020)
- [Non-Parametric Calibration for Classification](https://mlsanity.com/paper/1906.04933/) (2019)
- [Calibrated Prediction with Covariate Shift via Unsupervised Domain Adaptation](https://mlsanity.com/paper/2003.00343/) (2020)
- [Transferable Calibration with Lower Bias and Variance in Domain Adaptation](https://mlsanity.com/paper/2007.08259/) (2020)
- [Robust Calibration with Multi-domain Temperature Scaling](https://mlsanity.com/paper/2206.02757/) (2022)
- [Unsupervised Calibration under Covariate Shift](https://mlsanity.com/paper/2006.16405/) (2020)
- [Label-Free Model Evaluation with Semi-Structured Dataset Representations](https://mlsanity.com/paper/2112.00694/) (2021)

## Built on by (most cited first, 1 of 1)

- [Target-Agnostic Calibration under Distribution Shift with Frequency-Aware Gradient Rectification](https://mlsanity.com/paper/2508.19830/) (2025)

## Abstract (arXiv)

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.