  We present a novel approach for generating minority samples that live on
low-density regions of a data manifold. Our framework is built upon diffusion
models, leveraging the principle of guided sampling that incorporates an
arbitrary energy-based guidance during inference time. The key defining feature
of our sampler lies in its \emph{self-contained} nature, \ie, implementable
solely with a pretrained model. This distinguishes our sampler from existing
techniques that require expensive additional components (like external
classifiers) for minority generation. Specifically, we first estimate the
likelihood of features within an intermediate latent sample by evaluating a
reconstruction loss w.r.t. its posterior mean. The generation then proceeds
with the minimization of the estimated likelihood, thereby encouraging the
emergence of minority features in the latent samples of subsequent timesteps.
To further improve the performance of our sampler, we provide several
time-scheduling techniques that properly manage the influence of guidance over
inference steps. Experiments on benchmark real datasets demonstrate that our
approach can greatly improve the capability of creating realistic
low-likelihood minority instances over the existing techniques without the
reliance on costly additional elements. Code is available at
\url{https://github.com/soobin-um/sg-minority}.
