Text-to-image generative models have enabled high-resolution image synthesis across different domains, but require users to specify the content they wish to generate. In this paper, we consider the inverse problem -- given a collection of different images, can we discover the generative concepts that represent each image? We present an unsupervised approach to discover generative concepts from a collection of images, disentangling different art styles in paintings, objects, and lighting from kitchen scenes, and discovering image classes given ImageNet images. We show how such generative concepts can accurately represent the content of images, be recombined and composed to generate new artistic and hybrid images, and be further used as a representation for downstream classification tasks.

翻译：文本到图像生成模型已实现跨不同领域的高分辨率图像合成，但要求用户指定其希望生成的内容。本文考虑逆问题——给定一组不同图像，我们能否发现表征每幅图像的生成性概念？我们提出一种无监督方法，从图像集合中发现生成性概念，将绘画中的不同艺术风格、物体与厨房场景中的光照进行解耦，并基于ImageNet图像发现图像类别。我们展示了此类生成性概念如何准确表征图像内容，如何重组与组合以生成新的艺术与混合图像，并进一步用作下游分类任务的表示。