AI RESEARCH
Let Synthetic Data Shine: Domain Reassembly and Soft-Fusion for Single Domain Generalization
arXiv CS.CV
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ArXi:2503.13617v2 Announce Type: replace Single Domain Generalization (SDG) aims to train models that maintain consistent performance across diverse scenarios using data from a single source. While latent diffusion models (LDMs) show promise for augmenting limited source data, our analysis reveals that directly employing synthetic data may not only fail to provide benefits but can actually compromise performance due to substantial feature distribution discrepancies between synthetic and real target domains.