  Most recent works of test-time adaptation (TTA) aim to alleviate domain shift
problems by re-training source classifiers in each domain. On the other hand,
the emergence of the diffusion model provides another solution to TTA, which
directly maps the test data from the target domain to the source domain based
on a diffusion model pre-trained in the source domain. The source classifier
does not need to be fine-tuned. However, 1) the semantic information loss from
test data to the source domain and 2) the model shift between the source
classifier and diffusion model would prevent the diffusion model from mapping
the test data back to the source domain correctly. In this paper, we propose a
novel guidance-based diffusion-driven adaptation (GDDA) to overcome the data
shift and let the diffusion model find a better way to go back to the source.
Concretely, we first propose detail and global guidance to better keep the
common semantics of the test and source data. The two guidance include a
contrastive loss and mean squared error to alleviate the information loss by
fully exploring the diffusion model and the test data. Meanwhile, we propose a
classifier-aware guidance to reduce the bias caused by the model shift, which
can incorporate the source classifier's information into the generation process
of the diffusion model. Extensive experiments on three image datasets with
three classifier backbones demonstrate that GDDA significantly performs better
than the state-of-the-art baselines. On CIFAR-10C, CIFAR-100C, and ImageNetC,
GDDA achieves 11.54\%, 19.05\%, and 11.63\% average accuracy improvements,
respectively. GDDA even achieves equal performance compared with methods of
re-training classifiers. The code is available in the supplementary material.
