论文标题
使用胶囊网络进行发电机体系结构的胶囊gan
Capsule GAN Using Capsule Network for Generator Architecture
论文作者
论文摘要
本文提出了胶囊，这是一种使用胶囊网络的生成对抗网络，不仅在鉴别器中，而且在发电机中也是如此。最近，已经对生成对抗网络（GAN）进行了深入研究。但是，很难通过gan生成图像。因此，甘斯有时会产生质量差的图像。这些gan使用卷积神经网络（CNN）。但是，CNN的缺陷是，图像特征之间的关系信息可能会丢失。 Hinton于2017年提出的胶囊网络克服了CNN的缺陷。胶囊GAN报告了先前在鉴别器中使用胶囊网络。但是，在先前的研究中报道的胶囊不使用胶囊网络，而是在像DCGAN这样的发电机体系结构中使用CNN。本文介绍了在发电机中使用胶囊网络的两种方法。一种是将来自鉴别器的DigitCaps层用作发电机的输入。 DigitCaps层是胶囊网络的输出层。它具有鉴别器的输入图像的特征。另一个是使用发电机胶囊网络中识别过程的反向操作。我们将本文中提出的胶囊与使用CNN和Capsule GAN的常规GAN进行比较，该胶囊仅在鉴别器中使用胶囊网络。数据集是MNIST，时尚摄影和颜色图像。我们表明，仅在鉴别器中，使用CNN和GAN使用CNN和GAN优于GAN。本文提出的胶囊的体系结构是使用胶囊网络的基本体系结构。因此，我们可以将现有的改进技术应用于胶囊。
This paper presents Capsule GAN, a Generative adversarial network using Capsule Network not only in the discriminator but also in the generator. Recently, Generative adversarial networks (GANs) has been intensively studied. However, generating images by GANs is difficult. Therefore, GANs sometimes generate poor quality images. These GANs use convolutional neural networks (CNNs). However, CNNs have the defect that the relational information between features of the image may be lost. Capsule Network, proposed by Hinton in 2017, overcomes the defect of CNNs. Capsule GAN reported previously uses Capsule Network in the discriminator. However, instead of using Capsule Network, Capsule GAN reported in previous studies uses CNNs in generator architecture like DCGAN. This paper introduces two approaches to use Capsule Network in the generator. One is to use DigitCaps layer from the discriminator as the input to the generator. DigitCaps layer is the output layer of Capsule Network. It has the features of the input images of the discriminator. The other is to use the reverse operation of recognition process in Capsule Network in the generator. We compare Capsule GAN proposed in this paper with conventional GAN using CNN and Capsule GAN which uses Capsule Network in the discriminator only. The datasets are MNIST, Fashion-MNIST and color images. We show that Capsule GAN outperforms the GAN using CNN and the GAN using Capsule Network in the discriminator only. The architecture of Capsule GAN proposed in this paper is a basic architecture using Capsule Network. Therefore, we can apply the existing improvement techniques for GANs to Capsule GAN.
