About: Neural Cellular Automata Manifold

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An Entity of Type : schema:ScholarlyArticle, within Data Space : covidontheweb.inria.fr associated with source document(s)

Attributes	Values
type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
title	Neural Cellular Automata Manifold
Creator	Ruiz, Alejandro Moreno-Noguer, Francesc Vilalta, Armand
source	ArXiv
abstract	Very recently, a deep Neural Cellular Automata (NCA)[1] has been proposed to simulate the complex morphogenesis process with deep networks. This model learns to grow an image starting from a fixed single pixel. In this paper, we move a step further and propose a new model that extends the expressive power of NCA from a single image to an manifold of images. In biological terms, our approach would play the role of the transcription factors, modulating the mapping of genes into specific proteins that drive cellular differentiation, which occurs right before the morphogenesis. 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 cell's environment information. The proposed model also extends the capabilities of the NCA to a general purpose network, which can be used in a broad range of problems. We thoroughly evaluate our approach in a dataset of synthetic emojis and also in real images of CIFAR-10.
has issue date	2020-06-22(xsd:dateTime)
has license	arxiv
sha1sum (hex)	fbd11701c8a59d9f1d1654f8956f1cf5c8890168
resource representing a document's title	Neural Cellular Automata Manifold
resource representing a document's body	covid:fbd11701c8a59d9f1d1654f8956f1cf5c8890168#body_text
is schema:about of	named entity 'Neural' named entity 'terms' named entity 'cellular differentiation' named entity 'dataset' named entity 'fixed' »more»

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