Fourier Self-Adaptation for Transferring General Pretrained Models to Specific Domains

While pre-trained models in the general domain have proliferated, existing methods for transferring these models to specific domains often depend on source domain data for distribution alignment and are typically tailored for single tasks. We propose a source data-free approach, Fourier Self-Adaptation (FSA), which effectively adapts general models to a wide range of specific domains. Our method leverages the distinct properties of Fourier phase and amplitude: phase contains high-level structural and positional information, which is less affected by domain shifts, while amplitude contains details and brightness information, which is more affected by domain shifts. FSA adjusts the image distribution by initializing a trainable adaptive image from a normal distribution. It then interpolates the amplitude of the target domain image with that of the adaptive image, where the interpolation ratio is dynamically controlled by learnable weight and bias. During training, the model captures advanced phase information of the target image and refines the data distribution through amplitude interpolation. Additionally, a dual regularization loss constrains the model representation, encouraging it to focus on the intrinsic relationships of the target domain data while discarding irrelevant knowledge. We evaluate FSA using general pre-trained models on 11 unimodal image classification datasets and 6 multimodal visual question answering datasets, covering specific domains such as radiology, pathology, remote sensing, and art. Our method consistently achieves state-of-the-art performance across multiple datasets, with performance improvements ranging from 1% to 8% compared to basic pre-trained models. Source code are available at https://github.com/Alivelei/FSA.