Adapting models deployed to test distributions can mitigate the performance
degradation caused by distribution shifts. However, privacy concerns may render
model parameters inaccessible. One promising approach involves utilizing
zeroth-order optimization (ZOO) to train a data adaptor to adapt the test data
to fit the deployed models. Nevertheless, the data adaptor trained with ZOO
typically brings restricted improvements due to the potential corruption of
data features caused by the data adaptor. To address this issue, we revisit ZOO
in the context of test-time data adaptation. We find that the issue directly
stems from the unreliable estimation of the gradients used to optimize the data
adaptor, which is inherently due to the unreliable nature of the pseudo-labels
assigned to the test data. Based on this observation, we propose
pseudo-label-robust data adaptation (SODA) to improve the performance of data
adaptation. Specifically, SODA leverages high-confidence predicted labels as
reliable labels to optimize the data adaptor with ZOO for label prediction. For
data with low-confidence predictions, SODA encourages the adaptor to preserve
data information to mitigate data corruption. Empirical results indicate that
SODA can significantly enhance the performance of deployed models in the
presence of distribution shifts without requiring access to model parameters.

通过使用零阶优化方法（ZOO）进行众测数据适应测试，可以改进部署模型在分布变化时的性能降低问题。为了解决受数据适应器可能引起的数据特征破损问题，我们提出了基于伪标签的数据适应方法（SODA），可显著提升部署模型在分布变化下的性能。