  Compositional generalization is the capacity to recognize and imagine a large
amount of novel combinations from known components. It is a key in human
intelligence, but current neural networks generally lack such ability. This
report connects a series of our work for compositional generalization, and
summarizes an approach. The first part contains concepts and properties. The
second part looks into a machine learning approach. The approach uses
architecture design and regularization to regulate information of
representations. This report focuses on basic ideas with intuitive and
illustrative explanations. We hope this work would be helpful to clarify
fundamentals of compositional generalization and lead to advance artificial
intelligence.
