  Motivated by the interaction between cells, the recently introduced concept
of Neural Cellular Automata shows promising results in a variety of tasks. So
far, this concept was mostly used to generate images for a single scenario. As
each scenario requires a new model, this type of generation seems contradictory
to the adaptability of cells in nature. To address this contradiction, we
introduce a concept using different initial environments as input while using a
single Neural Cellular Automata to produce several outputs. Additionally, we
introduce GANCA, a novel algorithm that combines Neural Cellular Automata with
Generative Adversarial Networks, allowing for more generalization through
adversarial training. The experiments show that a single model is capable of
learning several images when presented with different inputs, and that the
adversarially trained model improves drastically on out-of-distribution data
compared to a supervised trained model.
