  Neural cellular automata represent an evolution of the traditional cellular
automata model, enhanced by the integration of a deep learning-based transition
function. This shift from a manual to a data-driven approach significantly
increases the adaptability of these models, enabling their application in
diverse domains, including content generation and artificial life. However,
their widespread application has been hampered by significant computational
requirements. In this work, we introduce the Latent Neural Cellular Automata
(LNCA) model, a novel architecture designed to address the resource limitations
of neural cellular automata. Our approach shifts the computation from the
conventional input space to a specially designed latent space, relying on a
pre-trained autoencoder. We apply our model in the context of image
restoration, which aims to reconstruct high-quality images from their degraded
versions. This modification not only reduces the model's resource consumption
but also maintains a flexible framework suitable for various applications. Our
model achieves a significant reduction in computational requirements while
maintaining high reconstruction fidelity. This increase in efficiency allows
for inputs up to 16 times larger than current state-of-the-art neural cellular
automata models, using the same resources.
