In this episode, we’re covering the paper "Denoising Diffusion Probabilistic Models". This framework offers a new way to generate high-quality images by gradually adding and removing noise in a two-step process. Unlike GANs, diffusion models are more stable and produce diverse results. The method has achieved state-of-the-art performance on datasets like CIFAR-10 and LSUN, paving the way for advancements in image generation and restoration. Stay tuned as we break down how this technique works and why it’s making waves in AI research.

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