Attention-based Neural Cellular Automata. (arXiv:2211.01233v1 [cs.CV])

Recent extensions of Cellular Automata (CA) have incorporated key ideas from
modern deep learning, dramatically extending their capabilities and catalyzing
a new family of Neural Cellular Automata (NCA) techniques. Inspired by
Transformer-based architectures, our work presents a new class of
$\textit{attention-based}$ NCAs formed using a spatially
localized$\unicode{x2014}$yet globally organized$\unicode{x2014}$self-attention
scheme. We introduce an instance of this class named $\textit{Vision
Transformer Cellular Automata}$ (ViTCA). We present quantitative and
qualitative results on denoising autoencoding across six benchmark datasets,
comparing ViTCA to a …

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