文献类型：期刊文献

英文题名：Research on Image-to-Image Translation with Capsule Network

作者：Ye, Jian[1]; Chang, Qing[1]; Jia, Xiaotian[1]

机构：[1] School of Information Science and Engineering, East China University of Science and Technology, Shanghai, 200030, China

年份：2019

卷号：11727 LNCS

起止页码：141

外文期刊名：Lecture Notes in Computer Science

收录：EI(收录号：20194107504035)

语种：英文

外文关键词：Chemical activation - Convolution - Deep learning

摘要：Deep learning technologies provide a unified translation framework for image-to-image translation. In particular, Convolution Neural Network (CNN) plays a decisive role because of its remarkable flexibility and performance. Recently, a new architecture called Capsule Network (CapsNet) was proposed to improve CNN. Capsule Network is able to preserve more input’s information, especially location information by it’s unique capsule structure and dynamic routing algorithm. In this paper, we propose Capsule conditional Generative Adversarial Network (CapscGAN) for performing image-to-image translation tasks. The proposed model utilizes CapsNet to encode image into one capsule called PixelCapsule and combines it with Markovian discriminator (PatchGAN) as discriminator. For suiting to translation tasks, we modify CapsNet’s structure and activation function. Through a series of experiments, we analyze effect of CapsNet’s activation function, dimension and application in discriminator. Multiple datasets’ results demonstrate that our model has higher translation quality than convolutional image translation framework. ? 2019, Springer Nature Switzerland AG.