From Symbols to Signals: Symbolic Variational Autoencoders

We introduce Symbolic Variational Autoencoders which generate images from symbols that represent semantic concepts. Unlike generic Variational Autoencoders (VAEs) or Generative Adversarial Networks (GANs), the latent distribution from the Symbolic Variational Autoencoder is discrete. The symbols are learned in a completely unsupervised manner by reconstructing images from symbolic encodings. We demonstrate the efficacy of our symbolic approach on the MNIST and FashionMNIST datasets. Results indicate that symbolic encodings naturally form a grammar, where unique strings of symbols map to different semantic concepts. We further explore how changing these symbols affects the final image that is generated.