提出概率框架，让模型在线适应测试分布变化。
A probabilistic framework for online test-time adaptation
- 基于状态空间模型构建概率框架
- 支持参数学习与时间演化建模
- 适合应对测试时分布偏移问题
本文提出一种概率框架，用于解决在线测试时自适应问题。在这些问题中，模型在有标签数据上训练，但在测试时需适应无标签数据，且假设训练与测试分布可能不同，即存在分布偏移。该框架基于状态空间建模架构，可对参数学习、参数时间演化、先验调优及预测进行统一描述。
原文摘要 · Abstract (English)
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.
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