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Diffusion models

Last updated Mar 30, 2023 Edit Source

Diffusion Models are generative models that work by destroying training data through the successive addition of Gaussian noise, and then learning to recover the data by reversing this noising process. After training, the Diffusion Model can be used to generate data by simply passing randomly sampled noise through the learned denoising process

See AI/Deep learning/Multimodal learning

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