Improved Test-Time Adaptation for Domain Generalization

Liang Chen, Yong Zhang, Yibing Song, Ying Shan, Lingqiao Liu

Abstract:

The main challenge in domain generalization (DG) is to
handle the distribution shift problem that lies between the
training and test data. Recent studies suggest that test-time
training (TTT), which adapts the learned model with test
data, might be a promising solution to the problem. Generally, a TTT strategy hinges its performance on two main
factors: selecting an appropriate auxiliary TTT task for updating and identifying reliable parameters to update during
the test phase. Both previous arts and our experiments indicate that TTT may not improve but be detrimental to the
learned model if those two factors are not properly considered. This work addresses those two factors by proposing
an Improved Test-Time Adaptation (ITTA) method. First, instead of heuristically defining an auxiliary objective, we propose a learnable consistency loss for the TTT task, which contains learnable parameters that can be adjusted toward better alignment between our TTT task and the main prediction
task. Second, we introduce additional adaptive parameters
for the trained model, and we suggest only updating the adaptive parameters during the test phase. Through extensive experiments, we show that the proposed two strategies are beneficial for the learned model (see Figure 1), and ITTA could
achieve superior performance to the current state-of-the-art
methods on several DG benchmarks