Latent Neural Cellular Automata for Resource-Efficient Image Restoration

arXiv:2403.15525v1 Announce Type: cross
Abstract: 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 …

abstract adaptability application arxiv cellular content generation cs.lg cs.ne data data-driven deep learning diverse domains eess.iv enabling evolution function image image restoration integration shift transition type