# Approaching Test Time Augmentation in the Context of Uncertainty Calibration for Deep Neural Networks

arXiv: https://arxiv.org/abs/2304.05104 · HTML: https://mlsanity.com/paper/2304.05104/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 39 references that are in the map.
- First posted on arXiv 11 Apr 2023.

## Builds on (most cited first, 22 of 22)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [On Calibration of Modern Neural Networks](https://mlsanity.com/paper/1706.04599/) (2017)
- [Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning](https://mlsanity.com/paper/1506.02142/) (2015)
- [Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles](https://mlsanity.com/paper/1612.01474/) (2016)
- [Benchmarking Neural Network Robustness to Common Corruptions and Perturbations](https://mlsanity.com/paper/1903.12261/) (2019)
- [Can You Trust Your Model's Uncertainty? Evaluating Predictive Uncertainty Under Dataset Shift](https://mlsanity.com/paper/1906.02530/) (2019)
- [When Does Label Smoothing Help?](https://mlsanity.com/paper/1906.02629/) (2019)
- [Regularizing Neural Networks by Penalizing Confident Output Distributions](https://mlsanity.com/paper/1701.06548/) (2017)
- [Measuring Calibration in Deep Learning](https://mlsanity.com/paper/1904.01685/) (2019)
- [Beyond temperature scaling: Obtaining well-calibrated multiclass probabilities with Dirichlet calibration](https://mlsanity.com/paper/1910.12656/) (2019)
- [AID: A Benchmark Dataset for Performance Evaluation of Aerial Scene Classification](https://mlsanity.com/paper/1608.05167/) (2016)
- [Aleatoric uncertainty estimation with test-time augmentation for medical image segmentation with convolutional neural networks](https://mlsanity.com/paper/1807.07356/) (2018)
- [Uncertainty Quantification and Deep Ensembles](https://mlsanity.com/paper/2007.08792/) (2020)
- [Better Aggregation in Test-Time Augmentation](https://mlsanity.com/paper/2011.11156/) (2020)
- [Confidence-Aware Learning for Deep Neural Networks](https://mlsanity.com/paper/2007.01458/) (2020)
- [Multivariate Confidence Calibration for Object Detection](https://mlsanity.com/paper/2004.13546/) (2020)
- [Calibration tests in multi-class classification: A unifying framework](https://mlsanity.com/paper/1910.11385/) (2019)
- [Intra Order-preserving Functions for Calibration of Multi-Class Neural Networks](https://mlsanity.com/paper/2003.06820/) (2020)
- [Greedy Policy Search: A Simple Baseline for Learnable Test-Time Augmentation](https://mlsanity.com/paper/2002.09103/) (2020)
- [Learning Loss for Test-Time Augmentation](https://mlsanity.com/paper/2010.11422/) (2020)
- [Top-label calibration and multiclass-to-binary reductions](https://mlsanity.com/paper/2107.08353/) (2021)
- [A Geometric Perspective towards Neural Calibration via Sensitivity Decomposition](https://mlsanity.com/paper/2110.14577/) (2021)

## Built on by (most cited first, 7 of 7)

- [Harnessing Uncertainty-aware Bounding Boxes for Unsupervised 3D Object Detection](https://mlsanity.com/paper/2408.00619/) (2024)
- [Test-Time Augmentation Meets Variational Bayes](https://mlsanity.com/paper/2409.12587/) (2024)
- [Consistency Calibration: Improving Uncertainty Calibration via Consistency among Perturbed Neighbors](https://mlsanity.com/paper/2410.12295/) (2024)
- [Test-time augmentation improves efficiency in conformal prediction](https://mlsanity.com/paper/2505.22764/) (2025)
- [Homographic Navigation: Geometry-Driven Camera Guidance for Deterministic Planar Capture](https://mlsanity.com/paper/2606.22834/) (2026)
- [GATTA: Graph Active Learning with Test-Time Augmentation](https://mlsanity.com/paper/2608.15084/) (2026)
- [Improving Calibration of Black-Box Radiology AI Using Test-Time Augmentation](https://mlsanity.com/paper/2609.29931/) (2026)

## Abstract (arXiv)

With the rise of Deep Neural Networks, machine learning systems are nowadays ubiquitous in a number of real-world applications, which bears the need for highly reliable models. This requires a thorough look not only at the accuracy of such systems, but also at their predictive uncertainty. Hence, we propose a novel technique (with two different variations, named M-ATTA and V-ATTA) based on test time augmentation, to improve the uncertainty calibration of deep models for image classification. By leveraging na adaptive weighting system, M/V-ATTA improves uncertainty calibration without affecting the model's accuracy. The performance of these techniques is evaluated by considering diverse metrics related to uncertainty calibration, demonstrating their robustness. Empirical results, obtained on CIFAR-10, CIFAR-100, Aerial Image Dataset, as well as in two different scenarios under distribution-shift, indicate that the proposed methods outperform several state-of-the-art post-hoc calibration techniques. Furthermore, the methods proposed also show improvements in terms of predictive entropy on out-of-distribution samples. Code for M/V-ATTA available at: https://github.com/pedrormconde/MV-ATTA