Researchers From UT Austin and UC Berkeley Introduce Ambient Diffusion: An AI Framework To Train/Finetune Diffusion Models Given Only Corrupted Data As Input

For learning high-dimensional distributions and resolving inverse problems, generative diffusion models are emerging as flexible and potent frameworks. Text conditional foundation models like Dalle-2, Latent Diffusion, and Imagen have achieved remarkable performance in generic picture domains due to several recent advancements. Diffusion models have recently shown their ability to memorize samples from their training set. […]

The post Researchers From UT Austin and UC Berkeley Introduce Ambient Diffusion: An AI Framework To Train/Finetune Diffusion Models Given Only Corrupted Data As …

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