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Reducing Structural Hallucination in Conditional Diffusion Models through Local Image Generation
Conditional diffusion models struggle with out-of-distribution (OOD) features, leading to structural hallucinations in generated images. Our method alleviates this issue by performing separate diffusion processes for in-distribution (IND) and OOD regions, followed by a fusion module to produce coherent outputs.
Leveraging Steganography to Improve Semantic Consistency in Non-Bijective Image-to-Image Translation
StegoGAN leverages steganography to prevent the hallucination of spurious features when translating between image domains with non-bijective class mappings.