Invertible Neural Cellular Automata

We introduce invertible Neural Cellular Automata (iNCA), a novel recursive, self-organizing and space variant generative neural architecture, which is able to learn bi-directional mappings with Cellular Automata. By modeling an additional latent target distribution, iNCA allow to learn ambiguous inverse mappings and the estimation of posterior probabilities. Compared to previous invertible neural network architectures, iNCAs show a significant improvement in inverse mapping capabilities for data organized in discrete grid structures. We demonstrate the general theoretical capabilities of iNCA empirically on a series of image generation and classification tasks.