Pruning then Reweighting: Towards Data-Efficient Training of Diffusion Models
Despite the remarkable generation capabilities of Diffusion Models (DMs), conducting training and inference remains computationally expensive. Previous works have been devoted to accelerating diffusion sampling, but achieving data-efficient diffusion training has often been overlooked. In this work,...
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Main Authors | , , , |
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Format | Journal Article |
Language | English |
Published |
27.09.2024
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Subjects | |
Online Access | Get full text |
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