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

Turn Down the Noise: Leveraging Diffusion Models for Test-time Adaptation via Pseudo-label Ensembling