$\mu$NCA: Texture Generation with Ultra-Compact Neural Cellular Automata

Algorithmic We study the problem of example-based procedural texture information theory defines Kolmogorov complexity synthesis using highly compact models. Given a sample [17, 42] as the shortest computer program generating image, we use differentiable programming to train a a certain output. There exist parallels to the neighbouring generative process, parameterised by a recurrent Neural fields of compression and information theory, which Cellular Automata (NCA) rule. Contrary to the common handle the more specific instance of transmitting information belief that neural networks should be significantly overparameterised, using the fewest number of bits. Outside academia, we demonstrate that our model architecture work in the demo-scene [41] has established a long tradition and training procedure allows for representing complex texture of encoding complex geometries and rendering algorithms patterns using just a few hundred learned parameters, into extremely short programmatic code and small making their expressivity comparable to hand-engineered compiled binaries.