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Few-Shot Diffusion Models
[article]
2022
arXiv
pre-print
Denoising diffusion probabilistic models (DDPM) are powerful hierarchical latent variable models with remarkable sample generation quality and training stability. These properties can be attributed to parameter sharing in the generative hierarchy, as well as a parameter-free diffusion-based inference procedure. In this paper, we present Few-Shot Diffusion Models (FSDM), a framework for few-shot generation leveraging conditional DDPMs. FSDMs are trained to adapt the generative process
arXiv:2205.15463v1
fatcat:imyl3zsgqfcalguhgtpzycbraa