  This survey includes systematic generalization and a history of how machine
learning addresses it. We aim to summarize and organize the related information
of both conventional and recent improvements. We first look at the definition
of systematic generalization, then introduce Classicist and Connectionist. We
then discuss different types of Connectionists and how they approach the
generalization. Two crucial problems of variable binding and causality are
discussed. We look into systematic generalization in language, vision, and VQA
fields. Recent improvements from different aspects are discussed. Systematic
generalization has a long history in artificial intelligence. We could cover
only a small portion of many contributions. We hope this paper provides a
background and is beneficial for discoveries in future work.
