arXiv Machine Learning
SPeaR: Test-Time Adaptation with Steering Primitives for Realigning Representations
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On the Interaction Between Model Compression and Test-Time Adaptation
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arXiv Computer Vision
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arXiv:2603.14254v2 Announce Type: replace Abstract: Test-time adaptation (TTA) aims to improve model robustness under distribution shifts by adapting to unlabeled test data, but most existing methods...
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Continual Test-Time Adaptation in Computer Vision: Methods, Benchmarks, and Future Directions
The paper surveys Continual Test-Time Adaptation (CTTA), a framework that adapts pretrained computer‑vision models to non‑stationary target distributions without source data or labeled targets, while avoiding catastrophic forgetting and error accumulation. It formally defines the CTTA problem, categorizes existing methods into optimization‑based, parameter‑efficient, and architecture‑based families, and reviews representative techniques and benchmarks across standard evaluation settings. The survey also outlines current limitations and proposes future research directions, such as adapting foundation models and black‑box systems.
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Continual Test-Time Adaptation in Computer Vision: Methods, Benchmarks, and Future Directions
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 AI
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DOME: Learning Transferable Domain Variables from Sparse Supervision for Test-Time Adaptation
arXiv:2606. 07646v1 Announce Type: cross Abstract: Test-time adaptation (TTA) aims to align a model to shifting test domains using only unlabeled streaming data.
arXiv Machine Learning
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Logit-Origin Centering for Singleton Test-Time Adaptation
arXiv:2608. 01074v1 Announce Type: new Abstract: Tabular data is used extensively in many real-world use cases.