Generating High Fidelity Data from Low-density Regions using Diffusion Models

• URL: http://arxiv.org/abs/2203.17260v1
• Date: Thu, 31 Mar 2022 17:56:25 GMT
• Title: Generating High Fidelity Data from Low-density Regions using Diffusion Models
• Authors: Vikash Sehwag, Caner Hazirbas, Albert Gordo, Firat Ozgenel, Cristian Canton Ferrer
• Abstract summary: We leverage diffusion process based generative models to synthesize novel images from low-density regions. We modify the sampling process to guide it towards low-density regions while simultaneously maintaining the fidelity of synthetic data.
• Score: 15.819414178363571
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Our work focuses on addressing sample deficiency from low-density regions of data manifold in common image datasets. We leverage diffusion process based generative models to synthesize novel images from low-density regions. We observe that uniform sampling from diffusion models predominantly samples from high-density regions of the data manifold. Therefore, we modify the sampling process to guide it towards low-density regions while simultaneously maintaining the fidelity of synthetic data. We rigorously demonstrate that our process successfully generates novel high fidelity samples from low-density regions. We further examine generated samples and show that the model does not memorize low-density data and indeed learns to generate novel samples from low-density regions.

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