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Deep neural networks can learn powerful prior probability models for images, as evidenced by the high-quality generations obtained with recent score-based diffusion methods.
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It has potential: Gradient-driven denoisers for convergent solutions to inverse problems
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Diffusion models beat GAN on image synthesis
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