神经元胞自动机流形
Neural Cellular Automata Manifold
摘要：
最近，神经元胞自动机（NCA）被提出来模拟深度网络的形态发生过程。NCA学习从固定的单个像素开始生长图像。在这项工作中，我们表明，神经网络（NN）架构的NCA可以封装在一个更大的NN。这使我们能够提出一个新的模型，编码的NCA流形，他们每个人都能够生成一个独特的图像。因此，我们有效地学习了CA的嵌入空间，这显示了泛化能力。我们通过在自动编码器架构中引入动态卷积来实现这一点，这是第一次用于连接两个不同的信息源，编码和细胞环境信息。在生物学方面，我们的方法将发挥转录因子的作用，将基因映射到驱动细胞分化的特定蛋白质中，这发生在形态发生之前。我们在合成表情符号数据集和CIFAR10的真实图像中彻底评估了我们的方法。我们的模型引入了一个通用网络，它可以用于图像生成以外的广泛问题。
英文摘要：
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.
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