  Real-world vision models in dynamic environments face rapid shifts in domain
distributions, leading to decreased recognition performance. Continual
test-time adaptation (CTTA) directly adjusts a pre-trained source
discriminative model to these changing domains using test data. A highly
effective CTTA method involves applying layer-wise adaptive learning rates, and
selectively adapting pre-trained layers. However, it suffers from the poor
estimation of domain shift and the inaccuracies arising from the pseudo-labels.
In this work, we aim to overcome these limitations by identifying layers
through the quantification of model prediction uncertainty without relying on
pseudo-labels. We utilize the magnitude of gradients as a metric, calculated by
backpropagating the KL divergence between the softmax output and a uniform
distribution, to select layers for further adaptation. Subsequently, for the
parameters exclusively belonging to these selected layers, with the remaining
ones frozen, we evaluate their sensitivity in order to approximate the domain
shift, followed by adjusting their learning rates accordingly. Overall, this
approach leads to a more robust and stable optimization than prior approaches.
We conduct extensive image classification experiments on CIFAR-10C, CIFAR-100C,
and ImageNet-C and demonstrate the efficacy of our method against standard
benchmarks and prior methods.
