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arXiv Machine Learning By Damian S\'ojka, Marc Masana, Bart{\l}omiej Twardowski, Sebastian Cygert

Rethinking the Teacher-Student Framework for Test-Time Adaptation

Read the original on arXiv Machine Learning →

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.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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