Language-Guided Diffusion for Domain Generalization

Domain generalization (DG) addresses the challenge of training machine learning models that generalize effectively to unseen target domains exhibiting distributional shifts. Traditional data augmentation techniques, while useful, often fail to adequately simulate the novel domain characteristics necessary for robust DG. We introduce a novel data augmentation framework leveraging the synergistic power of Large Language Models (LLMs) and diffusion models to generate diverse and realistic training data for DG. Our method employs LLMs to create creative prompts that encapsulate new domain styles, which are then used by diffusion models to synthesize high-fidelity images representative of these unseen domains. Furthermore, we integrate a CLIP-guided diversity analysis to ensure that the generated data effectively enhances model generalization while maintaining computational efficiency. Experiments on the PACS dataset show that our method significantly outperforms traditional techniques.