# SEVA: Leveraging Single-Step Ensemble of Vicinal Augmentations for Test-Time Adaptation

arXiv: https://arxiv.org/abs/2505.04087 · HTML: https://mlsanity.com/paper/2505.04087/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 27 of its 53 references that are in the map.
- First posted on arXiv 7 May 2025.

## Builds on (most cited first, 27 of 27)

- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift](https://mlsanity.com/paper/1502.03167/) (2015)
- [Layer Normalization](https://mlsanity.com/paper/1607.06450/) (2016)
- [Benchmarking Neural Network Robustness to Common Corruptions and Perturbations](https://mlsanity.com/paper/1903.12261/) (2019)
- [Non-local Neural Networks](https://mlsanity.com/paper/1711.07971/) (2017)
- [Unsupervised Domain Adaptation by Backpropagation](https://mlsanity.com/paper/1409.7495/) (2014)
- [Improved Regularization of Convolutional Neural Networks with Cutout](https://mlsanity.com/paper/1708.04552/) (2017)
- [Group Normalization](https://mlsanity.com/paper/1803.08494/) (2018)
- [WILDS: A Benchmark of in-the-Wild Distribution Shifts](https://mlsanity.com/paper/2012.07421/) (2020)
- [Random Erasing Data Augmentation](https://mlsanity.com/paper/1708.04896/) (2017)
- [AugMix: A Simple Data Processing Method to Improve Robustness and Uncertainty](https://mlsanity.com/paper/1912.02781/) (2019)
- [Maximum Classifier Discrepancy for Unsupervised Domain Adaptation](https://mlsanity.com/paper/1712.02560/) (2017)
- [Deep Visual Domain Adaptation: A Survey](https://mlsanity.com/paper/1802.03601/) (2018)
- [Efficient Test-Time Model Adaptation without Forgetting](https://mlsanity.com/paper/2204.02610/) (2022)
- [Towards Stable Test-Time Adaptation in Dynamic Wild World](https://mlsanity.com/paper/2302.12400/) (2023)
- [MEMO: Test Time Robustness via Adaptation and Augmentation](https://mlsanity.com/paper/2110.09506/) (2021)
- [ClassMix: Segmentation-Based Data Augmentation for Semi-Supervised Learning](https://mlsanity.com/paper/2007.07936/) (2020)
- [Robust Test-Time Adaptation in Dynamic Scenarios](https://mlsanity.com/paper/2303.13899/) (2023)
- [Learning Data Augmentation Strategies for Object Detection](https://mlsanity.com/paper/1906.11172/) (2019)
- [EcoTTA: Memory-Efficient Continual Test-time Adaptation via Self-distilled Regularization](https://mlsanity.com/paper/2303.01904/) (2023)
- [Data Augmentation by Pairing Samples for Images Classification](https://mlsanity.com/paper/1801.02929/) (2018)
- [MT3: Meta Test-Time Training for Self-Supervised Test-Time Adaption](https://mlsanity.com/paper/2103.16201/) (2021)
- [Adapting Object Detectors with Conditional Domain Normalization](https://mlsanity.com/paper/2003.07071/) (2020)
- [MixNorm: Test-Time Adaptation Through Online Normalization Estimation](https://mlsanity.com/paper/2110.11478/) (2021)
- [Continual Test-time Domain Adaptation via Dynamic Sample Selection](https://mlsanity.com/paper/2310.03335/) (2023)
- [Test time Adaptation through Perturbation Robustness](https://mlsanity.com/paper/2110.10232/) (2021)
- [Survey: Image Mixing and Deleting for Data Augmentation](https://mlsanity.com/paper/2106.07085/) (2021)

## Built on by (most cited first, 2 of 2)

- [Adaptive Dual Uncertainty Optimization: Boosting Monocular 3D Object Detection under Test-Time Shifts](https://mlsanity.com/paper/2508.20488/) (2025)
- [Sparse Reasoning is Enough: Biological-Inspired Framework for Video Anomaly Detection with Large Pre-trained Models](https://mlsanity.com/paper/2511.17094/) (2025)

## Abstract (arXiv)

Test-Time adaptation (TTA) aims to enhance model robustness against distribution shifts through rapid model adaptation during inference. While existing TTA methods often rely on entropy-based unsupervised training and achieve promising results, the common practice of a single round of entropy training is typically unable to adequately utilize reliable samples, hindering adaptation efficiency. In this paper, we discover augmentation strategies can effectively unleash the potential of reliable samples, but the rapidly growing computational cost impedes their real-time application. To address this limitation, we propose a novel TTA approach named Single-step Ensemble of Vicinal Augmentations (SEVA), which can take advantage of data augmentations without increasing the computational burden. Specifically, instead of explicitly utilizing the augmentation strategy to generate new data, SEVA develops a theoretical framework to explore the impacts of multiple augmentations on model adaptation and proposes to optimize an upper bound of the entropy loss to integrate the effects of multiple rounds of augmentation training into a single step. Furthermore, we discover and verify that using the upper bound as the loss is more conducive to the selection mechanism, as it can effectively filter out harmful samples that confuse the model. Combining these two key advantages, the proposed efficient loss and a complementary selection strategy can simultaneously boost the potential of reliable samples and meet the stringent time requirements of TTA. The comprehensive experiments on various network architectures across challenging testing scenarios demonstrate impressive performances and the broad adaptability of SEVA. The code will be publicly available.