  We explore the problem of generating minority samples using diffusion models.
The minority samples are instances that lie on low-density regions of a data
manifold. Generating a sufficient number of such minority instances is
important, since they often contain some unique attributes of the data.
However, the conventional generation process of the diffusion models mostly
yields majority samples (that lie on high-density regions of the manifold) due
to their high likelihoods, making themselves ineffective and time-consuming for
the minority generating task. In this work, we present a novel framework that
can make the generation process of the diffusion models focus on the minority
samples. We first highlight that Tweedie's denoising formula yields favorable
results for majority samples. The observation motivates us to introduce a
metric that describes the uniqueness of a given sample. To address the inherent
preference of the diffusion models w.r.t. the majority samples, we further
develop minority guidance, a sampling technique that can guide the generation
process toward regions with desired likelihood levels. Experiments on benchmark
real datasets demonstrate that our minority guidance can greatly improve the
capability of generating high-quality minority samples over existing generative
samplers. We showcase that the performance benefit of our framework persists
even in demanding real-world scenarios such as medical imaging, further
underscoring the practical significance of our work. Code is available at
https://github.com/soobin-um/minority-guidance.
