SODA: Robust Training of Test-Time Data Adaptors
Zige Wang Yonggang Zhang Zhen Fang Long Lan Wenjing Yang Bo Han
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.