The paper investigates the teacher‑student framework used in Test‑Time Adaptation (TTA) and questions the common practice of updating the teacher via an exponential moving average of the student. The authors demonstrate that error accumulation still occurs, especially over longer sequences, and propose an intransigent teacher that remains fixed. This modification yields significant performance gains across multiple datasets, longer scenarios, and various architectures, including semantic segmentation, while also improving robustness to hyperparameter changes.
By Damian S\'ojka, Marc Masana, Bart{\l}omiej Twardowski, Sebastian Cygert
arXiv:2606. 31420v1 Announce Type: new Abstract: Test-Time Adaptation (TTA) enables models trained on a source domain to adapt online to unlabeled test data under distribution shifts.
By Shaoyang Huang, Yashi Zhu, Yichen Yu, Lei Zhang, Zhang Yi, Tao He
arXiv:2511. 18468v2 Announce Type: replace Abstract: Continual Test-Time Adaptation (CTTA) is crucial for deploying models in real-world applications with unseen, evolving target domains.
By Md Akil Raihan Iftee, Mir Sazzat Hossain, Rakibul Hasan Rajib, Tariq Iqbal, Md Mofijul Islam, M Ashraful Amin, Amin Ahsan Ali, AKM Mahbubur Rahman
Deep neural nets achieve remarkable performance when training and test data share the same distribution, but this assumption frequently breaks in real-world deployment, where data undergoes continual distributional shifts. Continual Test-Time Adaptation (CTTA) addresses this challenge by adapting pretrained models to non-stationary target distributions on-the-fly, without access to source data or labeled targets, while mitigating two critical failure modes: catastrophic forgetting of source knowledge and error accumulation from noisy pseudo-labels over extended time horizons.
arXiv:2609.37687v1 Announce Type: new
Abstract: Active test-time adaptation (ATTA) improves robustness under distribution shift by updating a deployed model during inference while selectively queryin...
By Muhammad Huzaifa, Lea Sch\"onherr, Thorsten Eisenhofer
DESA‑TTA introduces a low‑overhead test‑time adaptation technique for vision‑language object detectors that dynamically adjusts the teacher’s exponential moving average coefficient based on pseudo‑label confidence and box density. It also employs source anchoring to gradually pull student parameters back toward their pretrained values, mitigating cumulative drift. Experiments on multiple distribution shifts and two VLOD architectures demonstrate consistent gains, with a 14.5‑point AP₅₀ improvement on VOC‑C and a 55% higher inference throughput compared to the prior state‑of‑the‑art TTA method for YOLO‑World.
By Atif Belal, Lilian Hollard, Marco Pedersoli, Eric Granger
Recent work on distillation transfers the capabilities of large models to smaller ones often by updating the latter's parameters, through teacher forcing, on-policy distillation, and related training-time methods. In this paper, we ask whether such transfer can instead occur at test time.
arXiv:2608. 12307v1 Announce Type: cross Abstract: Recent work on distillation transfers the capabilities of large models to smaller ones often by updating the latter's parameters, through teacher forcing, on-policy distillation, and related training-time methods.
By Cheng Qian, Wenting Zhao, Liangwei Yang, Heng Wang, Jielin Qiu, Heng Ji, Silvio Savarese, Huan Wang, Shelby Heinecke
arXiv:2609.36655v1 Announce Type: new
Abstract: Continual test-time adaptation (CTTA) adapts a source model to an unlabeled test stream whose distribution may change over time. Existing TTA methods o...
By Youjia Zhang, Huiling Liu, Soyun Choi, Jaehong Yoon, Sungeun Hong
This paper introduces TestDG, an online test-time domain generalization framework for continual test-time adaptation (CTTA). TestDG learns features invariant to both current and past test domains during testing, using a new model architecture, adaptation strategy, and prototype selection/update mechanisms. It achieves state‑of‑the‑art results on four CTTA benchmarks and demonstrates superior generalization to unseen test domains.
By Sohyun Lee, Nayeong Kim, Juwon Kang, Seong Joon Oh, Suha Kwak
arXiv:2608. 01074v1 Announce Type: new Abstract: Tabular data is used extensively in many real-world use cases.
By Mayank Sharma, Rohit Kumar Mourya, Pratik Mazumder
arXiv:2609.24111v1 Announce Type: new
Abstract: Test-time adaptation (TTA) addresses distribution shift using only unlabeled test data. Existing methods typically adapt pretrained models by updating...
By Muhammad Sudipto Siam Dip, Ali Etemad