Free Domain Generalization

Source-free domain generalization (SFDG) aims to train machine learning models that generalize well to unseen data distributions without access to data from those distributions during training. Current research heavily leverages large vision-language models, particularly focusing on techniques like prompt engineering and style transfer using text prompts to simulate diverse data characteristics and learn domain-invariant representations. This approach addresses the limitations of traditional domain generalization methods that require source domain data, paving the way for more robust and adaptable AI systems across various applications. The success of SFDG holds significant implications for deploying AI models in real-world scenarios where comprehensive data from all possible domains is unavailable.