The seemingly infinite diversity of the natural world arises from a relatively small set of coherent rules, such as the laws of physics or chemistry. We conjecture that these rules give rise to regularities that can be discovered through primarily unsupervised experiences and represented as abstract concepts. If such representations are compositional and hierarchical, they can be recombined into an exponentially large set of new concepts. This paper describes SCAN (Symbol-Concept Association Network), a new framework for learning such abstractions in the visual domain. SCAN learns concepts through fast symbol association, grounding them in disentangled visual primitives that are discovered in an unsupervised manner. Unlike state of the art multimodal generative model baselines, our approach requires very few pairings between symbols and images and makes no assumptions about the form 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 visual concepts through symbolic instructions and learnt logical recombination operations. Such manipulations enable SCAN to break away from its training data distribution and imagine novel visual concepts through symbolically instructed recombination of previously learnt concepts.
We present a neural variational model for learning language-guided compositional visual concepts. Proposes a novel neural net architecture that learns object concepts by combining a beta-VAE and SCAN. This paper introduces a VAE-based model for translating between images and text, with their latent representation well-suited to applying symbolic operations, giving them a more expressive language for sampling images from text.  This paper proposes a new model called SCAN (Symbol-Concept Association Network) for hierarchical concept learning and allows for generalization to new concepts composed from existing concepts using logical operators.