🤖 AI Summary
This work addresses the challenge of distribution shift between training and test phases by proposing the first online test-time adaptation framework based on state space modeling. The method learns an initial model from labeled data during training and dynamically updates its parameters using unlabeled test data at inference time. It unifies parameter learning, temporal evolution, prior refinement, and prediction within a single probabilistic state space architecture. This formulation enables recursive online parameter updates and principled uncertainty quantification, yielding a general and robust adaptation mechanism. The approach provides both theoretical grounding and an effective solution for online prediction under distributional shifts.
📝 Abstract
This paper presents a probabilistic framework for online test-time adaptation problems. In them, a model is trained on labeled data but must adapt to unlabeled data at test time under the assumption that training and test distributions potentially differ, that is, there might have been a distributional shift. The framework is based on a state-space modelling architecture from which parameter learning, parameter time evolution, prior tuning, and prediction can be characterized.