Neural Cellular Automata Manifold
Alejandro Hernandez Ruiz, Armand Vilalta, Francesc Moreno-Noguer

TL;DR
This paper introduces a neural network model that encodes a manifold of Neural Cellular Automata, enabling the generation of diverse images and demonstrating generalization capabilities through an innovative auto-encoder with dynamic convolutions.
Contribution
The work presents a novel neural architecture that encapsulates multiple NCA behaviors within a single model using dynamic convolutions in an auto-encoder framework.
Findings
Successfully encodes a manifold of NCA for image generation
Demonstrates generalization on synthetic emojis and CIFAR10 images
Introduces a versatile network applicable beyond image synthesis
Abstract
Very recently, the Neural Cellular Automata (NCA) has been proposed to simulate the morphogenesis process with deep networks. NCA learns to grow an image starting from a fixed single pixel. In this work, we show that the neural network (NN) architecture of the NCA can be encapsulated in a larger NN. This allows us to propose a new model that encodes a manifold of NCA, each of them capable of generating a distinct image. Therefore, we are effectively learning an embedding space of CA, which shows generalization capabilities. We accomplish this by introducing dynamic convolutions inside an Auto-Encoder architecture, for the first time used to join two different sources of information, the encoding and cells environment information. In biological terms, our approach would play the role of the transcription factors, modulating the mapping of genes into specific proteins that drive cellular…
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