Recognizing elementary underlying concepts from observations
(disentanglement) and generating novel combinations of these concepts
(compositional generalization) are fundamental abilities for humans to support
rapid knowledge learning and generalize to new tasks, with which the deep
learning models struggle. Towards human-like intelligence, various works on
disentangled representation learning have been proposed, and recently some
studies on compositional generalization have been presented. However, few works
study the relationship between disentanglement and compositional
generalization, and the observed results are inconsistent. In this paper, we
study several typical disentangled representation learning works in terms of
both disentanglement and compositional generalization abilities, and we provide
an important insight: vector-based representation (using a vector instead of a
scalar to represent a concept) is the key to empower both good disentanglement
and strong compositional generalization. This insight also resonates the
neuroscience research that the brain encodes information in neuron population
activity rather than individual neurons. Motivated by this observation, we
further propose a method to reform the scalar-based disentanglement works
($\beta$-TCVAE and FactorVAE) to be vector-based to increase both capabilities.
We investigate the impact of the dimensions of vector-based representation and
one important question: whether better disentanglement indicates higher
compositional generalization. In summary, our study demonstrates that it is
possible to achieve both good concept recognition and novel concept
composition, contributing an important step towards human-like intelligence.

在人工智能领域，学习并拥有类人类的智能需要处理认知的本质概念和生成这些概念的新的组合的能力，本文研究了这两种能力之间的关系，并发现基于向量的表示方法可以提高这两种能力，此观察结果与神经科学研究一致，为实现类人智能迈出了重要一步。