Test-time adaptation is the problem of adapting a source pre-trained model
using test inputs from a target domain without access to source domain data.
Most of the existing approaches address the setting in which the target domain
is stationary. Moreover, these approaches are prone to making erroneous
predictions with unreliable uncertainty estimates when distribution shifts
occur. Hence, test-time adaptation in the face of non-stationary target domain
shift becomes a problem of significant interest. To address these issues, we
propose a principled approach, PETAL (Probabilistic lifElong Test-time
Adaptation with seLf-training prior), which looks into this problem from a
probabilistic perspective using a partly data-dependent prior. A
student-teacher framework, where the teacher model is an exponential moving
average of the student model naturally emerges from this probabilistic
perspective. In addition, the knowledge from the posterior distribution
obtained for the source task acts as a regularizer. To handle catastrophic
forgetting in the long term, we also propose a data-driven model parameter
resetting mechanism based on the Fisher information matrix (FIM). Moreover,
improvements in experimental results suggest that FIM based data-driven
parameter restoration contributes to reducing the error accumulation and
maintaining the knowledge of recent domain by restoring only the irrelevant
parameters. In terms of predictive error rate as well as uncertainty based
metrics such as Brier score and negative log-likelihood, our method achieves
better results than the current state-of-the-art for online lifelong test time
adaptation across various benchmarks, such as CIFAR-10C, CIFAR-100C, ImageNetC,
and ImageNet3DCC datasets.

PETAL 是一种基于概率视角和部分数据相关先验的测试时间自适应方法，利用 Fisher 信息矩阵的数据驱动模型参数重置机制应对非平稳目标领域转移，实验结果表明 PETAL 在多个基准测试数据集中表现出更好的预测性能和不确定性指标。