Self-Improving Diffusion Classifiers with Minority Preference Optimization
Prior studies have demonstrated that diffusion classifiers achieve robust zero-shot classification performance. However, their effectiveness is strongly tied to the pretraining data distribution: they perform well in majority, high-density regions of the data manifold, but are significantly less accurate in minority, low-density regions. Although prior works on minority sampling have focused on generating more minority-like images, what minority sampling fundamentally enables beyond generation remains underexplored. In this paper, we reveal a direct relationship between minority sampling in generation and the perception capability of diffusion classifiers. Specifically, we show that enhan...
| Authors | Hyunsoo Kim, Jungmyung Wi, Soobin Um et al. |
|---|---|
| Primary category | cs.CV |
| Published | 4 July 2026 |
| Last updated at source | 4 July 2026 |
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