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. The noise-adaptive MMD is trained on data distributions corrupted by increasing levels of noise%2C obtained via a forward diffusion process%2C as commonly used in denoising diffusion probabilistic models. The result is a generalization of MMD Gradient Flow%2C which we call Diffusion-MMD-Gradient Flow or DMMD. The divergence training procedure is related to discriminator training in Generative Adversarial Networks (GAN)%2C but does not require adversarial training. We obtain competitive empirical performance in unconditional image generation on CIFAR10%2C MNIST%2C CELEB-A (64 x64) and LSUN Church (64 x 64). Furthermore%2C we demonstrate the validity of the approach when MMD is replaced by a lower bound on the KL divergence.?quality=80&w=800)
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