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 sufficient numbers 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 highly ineffective and
time-consuming for the 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 provide a new insight on the majority-focused nature of the
diffusion models: they denoise in favor of the 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 the
low-likelihood minority samples over existing generative frameworks including
the standard diffusion sampler.

本论文探讨用扩散模型生成少数族裔样本的问题，提出了一种新的框架来引导扩散模型的生成过程，侧重于生成低密度区域上的少数族裔样本。实验结果表明，与标准扩散采样器相比，我们的少数族裔引导技术可以极大地提高生成低概率少数族裔样本的能力。