The natural world is infinitely diverse, yet this diversity arises from a relatively small set of coherent properties and rules, such as the laws of physics or chemistry. We conjecture that biological intelligent systems are able to survive within their diverse environments by discovering the regularities that arise from these rules primarily through unsupervised experiences, and representing this knowledge as abstract concepts. Such representations possess useful properties of compositionality and hierarchical organisation, which allow intelligent agents to recombine afinite set of conceptual building blocks into an exponentially large set of useful new concepts. This paper describes SCAN (Symbol-Concept Association Network), a new framework for learning such concepts in the visual domain. We first use the previously published beta-VAE (Higgins et al., 2017a) architecture to learn a disentangled representation of the latent structure of the visual world, before training SCAN to extract abstract concepts grounded in such disentangled visual primitives through fast symbol association. Our approach requires very few pairings between symbols and images and makes no assumptions about the choice of symbol representations.Once trained, SCAN is capable of multimodal bi-directional inference, generating a diverse set of image samples from symbolic descriptions and vice versa. It also allows for traversal and manipulation of the implicit hierarchy of compositional visual concepts through symbolic instructions and learnt logical recombination operations. Such manipulations enable SCAN to invent and learn novel visual concepts through recombination of the few learnt concepts.

该论文介绍了一种名为SCAN的学习框架，可通过快速符号关联以无监督方式发现视觉基元并将其作为抽象概念来学习概念。SCAN还可以通过符号指令遍历和操作视觉概念的隐式层次结构，并通过逻辑组合操作创建视觉概念的新组合，并能够生成多模态双向推断的结果。

SCAN：学习分层组合视觉概念