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• W3206123670 abstract "Generative diffusion processes are an emerging and effective tool for image and speech generation. In the existing methods, the underlying noise distribution of the diffusion process is Gaussian noise. However, fitting distributions with more degrees of freedom could improve the performance of such generative models. In this work, we investigate other types of noise distribution for the diffusion process. Specifically, we introduce the Denoising Diffusion Gamma Model (DDGM) and show that noise from Gamma distribution provides improved results for image and speech generation. Our approach preserves the ability to efficiently sample state in the training diffusion process while using Gamma noise." @default.
• W3206123670 created "2021-10-25" @default.
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• W3206123670 date "2021-10-10" @default.
• W3206123670 modified "2023-09-27" @default.
• W3206123670 title "Denoising Diffusion Gamma Models." @default.
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• W3206123670 hasPublicationYear "2021" @default.
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