  The goal of test-time adaptation is to adapt a source-pretrained model to a
continuously changing target domain without relying on any source data.
Typically, this is either done by updating the parameters of the model (model
adaptation) using inputs from the target domain or by modifying the inputs
themselves (input adaptation). However, methods that modify the model suffer
from the issue of compounding noisy updates whereas methods that modify the
input need to adapt to every new data point from scratch while also struggling
with certain domain shifts. We introduce an approach that leverages a
pre-trained diffusion model to project the target domain images closer to the
source domain and iteratively updates the model via pseudo-label ensembling.
Our method combines the advantages of model and input adaptations while
mitigating their shortcomings. Our experiments on CIFAR-10C demonstrate the
superiority of our approach, outperforming the strongest baseline by an average
of 1.7% across 15 diverse corruptions and surpassing the strongest input
adaptation baseline by an average of 18%.
