Score-based Generative Models (SGMs) are a popular family of deep generative
models that achieves leading image generation quality. Earlier studies have
extended 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 issue is
rooted in unreliable gradients of the classifier and the inability to fully
utilize unlabeled data during training. We then propose to improve
classifier-guided SGMs by letting the classifier calibrate itself. 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 adopted to calibrate the classifier using both labeled
and unlabeled data. Empirical results validate that the proposed approach
significantly improves the conditional generation quality across different
percentages of labeled data. The improved performance makes the proposed
approach consistently superior to other conditional SGMs when using fewer
labeled data. The results confirm the potential of the proposed approach for
generative modeling with limited labeled data.

通过将分类器作为 SGM，并利用少量标记数据，提供条件生成。