We present Generative Adversarial Capsule Network (CapsuleGAN), a framework that uses capsule networks (CapsNets) instead of the standard convolutional neural networks (CNNs) as discriminators within the generative adversarial network (GAN) setting, while modeling image data. We provide guidelines for designing CapsNet discriminators and the updated GAN objective function, which incorporates the CapsNet margin loss, for training CapsuleGAN models. We show that CapsuleGAN outperforms convolutional-GAN at modeling image data distribution on the MNIST dataset of handwritten digits, evaluated on the generative adversarial metric and at semi-supervised image classification.

翻译：我们介绍“创能反动能力网络”(CapsuleGAN)这一框架,它使用胶囊网络(CapsNets)而不是标准的进化神经网络(CNNs)作为基因对抗网络(GAN)设置中的区别器,同时制作图像数据模型,我们为设计CapsNet歧视器和包含CapsNet差值损失的更新的GAN客观功能提供了指南,用于培训CapsNet差值损失模型。我们显示,CapsuleGAN在模拟MNIST手写数字数据集的图像数据分布时,在根据基因对抗指标和半监督图像分类进行评估时,比CapsuleGAN高。