  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.
