  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. Code is
available at https://github.com/liangchen527/ITTA.
