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 differentiation, which occurs right before the morphogenesis. We thoroughly evaluate our approach in a dataset of synthetic emojis and also in real images of CIFAR10. Our model introduces a general-purpose network, which can be used in a broad range of problems beyond image generation.
翻译:最近,我们建议神经细胞自动成像(NCA) 以深网络模拟光发过程。 NCA 学会从固定的单像素中培养图像。 在这项工作中,我们显示NCA的神经网络结构可以包在一个更大的NN。这使我们能够提出一个新的模型,将一个多元的NCA编码为NCA,其中每个元都能够生成一个截然不同的图像。因此,我们正在有效地学习CA的嵌入空间,该空间可以显示一般化能力。我们通过在Auto-Encoder结构中引入动态共变体来实现这一目标,这是第一次用来加入两个不同的信息来源,即编码和细胞环境信息。在生物方面,我们的方法将发挥抄录因素的作用,将基因映射成能够驱动细胞差异的具体蛋白质,这在细胞发芽之前就已经发生。我们正在对合成化的血化数据集和CIFAR10的真实图像中的方法进行彻底评估。 我们的模型引入了一个通用的网络,可以在图像生成之外的广泛范围内使用。