A vital aspect of human intelligence is the ability to compose increasingly
complex concepts out of simpler ideas, enabling both rapid learning and
adaptation of knowledge. In this paper we show that energy-based models can
exhibit this ability by directly combining probability distributions. Samples
from the combined distribution correspond to compositions of concepts. For
example, given a distribution for smiling faces, and another for male faces, we
can combine them to generate smiling male faces. This allows us to generate
natural images that simultaneously satisfy conjunctions, disjunctions, and
negations of concepts. We evaluate compositional generation abilities of our
model on the CelebA dataset of natural faces and synthetic 3D scene images. We
also demonstrate other unique advantages of our model, such as the ability to
continually learn and incorporate new concepts, or infer compositions of
concept properties underlying an image.

本文中，我们展示了由能量基模型直接组合概率分布从而展示人类智能的一个重要方面：从简单构想到复杂概念的组合能力。我们的模型能够生成自然图像，同时满足概念的合取、析取和否定等关系。我们在 CelebA 数据集和合成 3D 场景图像上评估了我们模型的组合生成能力，并展示了我们模型的其他优势，如持续学习和整合新概念，或推断图像潜在的概念属性组合。