MDIC uses a text-conditioned diffusion decoder and a supervised feature-mask generator on visual side information to achieve SOTA perceptual quality in distributed image compression at extremely low bitrates.
Gans trained by a two time-scale update rule converge to a local nash equilib- rium.Advances in Neural Information Processing Systems, 30, 2017
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GrOCE uses dynamic semantic graphs for online, training-free erasure of target concepts from diffusion model prompts via cluster identification and selective severing.
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Distributed Image Compression with Multimodal Side Information at Extremely Low Bitrates
MDIC uses a text-conditioned diffusion decoder and a supervised feature-mask generator on visual side information to achieve SOTA perceptual quality in distributed image compression at extremely low bitrates.
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GrOCE:Graph-Guided Online Concept Erasure for Text-to-Image Diffusion Models
GrOCE uses dynamic semantic graphs for online, training-free erasure of target concepts from diffusion model prompts via cluster identification and selective severing.