Test-time domain adaptation effectively adjusts the source domain model to
accommodate unseen domain shifts in a target domain during inference. However,
the model performance can be significantly impaired by continuous distribution
changes in the target domain and non-independent and identically distributed
(non-i.i.d.) test samples often encountered in practical scenarios. While
existing memory bank methodologies use memory to store samples and mitigate
non-i.i.d. effects, they do not inherently prevent potential model degradation.
To address this issue, we propose a resilient practical test-time adaptation
(ResiTTA) method focused on parameter resilience and data quality.
Specifically, we develop a resilient batch normalization with estimation on
normalization statistics and soft alignments to mitigate overfitting and model
degradation. We use an entropy-driven memory bank that accounts for timeliness,
the persistence of over-confident samples, and sample uncertainty for
high-quality data in adaptation. Our framework periodically adapts the source
domain model using a teacher-student model through a self-training loss on the
memory samples, incorporating soft alignment losses on batch normalization. We
empirically validate ResiTTA across various benchmark datasets, demonstrating
state-of-the-art performance.

通过在测试时进行领域适应，提出了一种抗干扰的实用测试时间适应（ResiTTA）方法，用于解决模型退化和数据质量问题。利用鲁棒批量归一化方法和软对齐策略缓解过拟合和模型退化，并采用熵驱动的记忆库存储高质量数据。通过教师 - 学生模型和自学习损失对模型进行周期性的适应，最终在多个基准数据集上取得了最先进的性能。