Self-Guided Generation of Minority Samples Using Diffusion Models
This addresses the challenge of generating minority samples for data augmentation or analysis, offering a more efficient method compared to prior approaches that require external classifiers.
The paper tackles the problem of generating minority samples from low-density regions of a data manifold using diffusion models, achieving improved capability in creating realistic low-likelihood instances over existing techniques without costly additional components.
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}.