Source-Free Controlled Adaptation of Teachers for Continual Test-Time Adaptation
In many real-world scenarios, encountering continual shifts in domain during inference is very common. Consequently, continual test-time adaptation (CTTA) techniques leveraging a teacher-student framework have gained prominence, allowing models to adapt continuously even after deployment. In such a framework, a weight-averaged mean teacher is used to produce pseudo-labels from test data for self-training. The mean teacher gets updated as an exponential moving average of the student parameters us
Record details
Published: 26 July 2026
Source: arXiv cs.LG
Category: Research
Topics: Children & education
Retrieved: 29 July 2026
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ethics.ai (26 July 2026), “Source-Free Controlled Adaptation of Teachers for Continual Test-Time Adaptation,” evidence record 14492, https://ethics.ai/record/14492 (originally published by arXiv cs.LG).
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