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Attention-based Neural Cellular Automata. (arXiv:2211.01233v1 [cs.CV])
Nov. 3, 2022, 1:12 a.m. | Mattie Tesfaldet, Derek Nowrouzezahrai, Christopher Pal
cs.LG updates on arXiv.org arxiv.org
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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