  Test-time adaptation (TTA) aims to enhance the performance of source-domain
pretrained models when tested on unknown shifted target domains. Traditional
TTA methods primarily adapt model weights based on target data streams, making
model performance sensitive to the amount and order of target data. Recently,
diffusion-driven TTA methods have demonstrated strong performance by using an
unconditional diffusion model, which is also trained on the source domain to
transform target data into synthetic data as a source domain projection. This
allows the source model to make predictions without weight adaptation. In this
paper, we argue that the domains of the source model and the synthetic data in
diffusion-driven TTA methods are not aligned. To adapt the source model to the
synthetic domain of the unconditional diffusion model, we introduce a
Synthetic-Domain Alignment (SDA) framework to fine-tune the source model with
synthetic data. Specifically, we first employ a conditional diffusion model to
generate labeled samples, creating a synthetic dataset. Subsequently, we use
the aforementioned unconditional diffusion model to add noise to and denoise
each sample before fine-tuning. This process mitigates the potential domain gap
between the conditional and unconditional models. Extensive experiments across
various models and benchmarks demonstrate that SDA achieves superior domain
alignment and consistently outperforms existing diffusion-driven TTA methods.
Our code is available at
https://github.com/SHI-Labs/Diffusion-Driven-Test-Time-Adaptation-via-Synthetic-Domain-Alignment.
