Search Results for author: Muneyoshi Inahara

Found 1 papers, 0 papers with code

Quasi-Taylor Samplers for Diffusion Generative Models based on Ideal Derivatives

no code implementations26 Dec 2021 Hideyuki Tachibana, Mocho Go, Muneyoshi Inahara, Yotaro Katayama, Yotaro Watanabe

Diffusion generative models have emerged as a new challenger to popular deep neural generative models such as GANs, but have the drawback that they often require a huge number of neural function evaluations (NFEs) during synthesis unless some sophisticated sampling strategies are employed.

Denoising Image Generation +1

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