Score-based generative models (SGMs) are a popular family of deep generative models that achieve leading image generation quality. Early studies extend SGMs to tackle class-conditional generation by coupling an unconditional SGM with the guidance of a trained classifier. Nevertheless, such classifier-guided SGMs do not always achieve accurate conditional generation, especially when trained with fewer labeled data. We argue that the problem is rooted in the classifier's tendency to overfit without coordinating with the underlying unconditional distribution. To make the classifier respect the unconditional distribution, we propose improving classifier-guided SGMs by letting the classifier regularize itself. The key idea of our proposed method is to use principles from energy-based models to convert the classifier into another view of the unconditional SGM. Existing losses for unconditional SGMs can then be leveraged to achieve regularization by calibrating the classifier's internal unconditional scores. The regularization scheme can be applied to not only the labeled data but also unlabeled ones to further improve the classifier. Across various percentages of fewer labeled data, empirical results show that the proposed approach significantly enhances conditional generation quality. The enhancements confirm the potential of the proposed self-calibration technique for generative modeling with limited labeled data.

翻译：基于分数的生成模型（SGMs）是一类流行的深度生成模型，可实现领先的图像生成质量。早期研究通过将无条件SGM与训练好的分类器引导结合，拓展SGM以处理类别条件生成。然而，这种分类器引导的SGM在标注数据较少时，往往无法实现精确的条件生成。我们认为问题根源在于分类器倾向于过拟合，且未与潜在的无条件分布协调。为使分类器尊重无条件分布，我们提出通过让分类器自身正则化来改进分类器引导的SGM。该方法的核心思想是利用基于能量模型的原理，将分类器转换为无条件SGM的另一种视角。随后可借助无条件SGM的现有损失函数，通过校准分类器内部的无条件分数实现正则化。该正则化方案不仅可应用于标注数据，还可用于未标注数据以进一步改进分类器。在多种少标注数据比例下，实验结果表明所提方法显著提升了条件生成质量。这些改进验证了所提出的自校准技术在有限标注数据生成建模中的潜力。