DLTTA: Dynamic Learning Rate for Test-time Adaptation on Cross-domain Medical Images

Understand

Test-time adaptation (TTA) has increasingly been an important topic to efficiently tackle the cross-domain distribution shift at test time for medical images from different institutions.

• Previous TTA methods have a common limitation of using a fixed learning rate for all the test samples.
• Such a practice would be sub-optimal for TTA, because test data may arrive sequentially therefore the scale of distribution shift would change frequently.
• To address this problem, we propose a novel dynamic learning rate adjustment method for test-time adaptation, called DLTTA, which dynamically modulates the amount of weights update for each test image to account for the differences in their distribution shift.