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. We propose improving classifier-guided SGMs by letting the classifier regularize itself to respect the unconditional distribution. Our key idea is to use principles from energy-based models to convert the classifier as another view of the unconditional SGM. Then, existing loss for the unconditional SGM can 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. Empirical results show that the proposed approach significantly improves conditional generation quality across various percentages of fewer labeled data. The results confirm the potential of the proposed approach 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，在图像生成质量方面达到领先水平。早期研究通过将无条件SGM与训练分类器的引导相结合，将SGM扩展至类别条件生成任务。然而，此类分类器引导的SGM在标注数据较少时难以实现准确的条件生成。我们认为问题的根源在于分类器倾向于过拟合，而未与潜在的无条件分布协同工作。我们提出通过让分类器进行自正则化以尊重无条件分布来改进分类器引导的SGM。核心思想是利用基于能量模型的原理，将分类器转化为无条件SGM的另一视角。进而借助无条件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。该正则化方案不仅适用于标注数据，还可应用于未标注数据以进一步改进分类器。实验结果表明，所提方法在不同比例的低标注数据设置下均显著提升了条件生成质量。这些结果验证了该方法在标注数据有限场景下进行生成建模的潜力。