Text-to-image synthesis is an important and challenging application of computer vision. Many interesting and meaningful text-to-image synthesis models have been put forward. However, most of the works pay attention to the quality of synthesis images, but rarely consider the size of these models. Large models contain many parameters and high delay, which makes it difficult to be deployed on mobile applications. To solve this problem, we propose an efficient architecture CPGAN for text-to-image generative adversarial networks (GAN) based on canonical polyadic decomposition (CPD). It is a general method to design the lightweight architecture of text-to-image GAN. To improve the stability of CPGAN, we introduce conditioning augmentation and the idea of autoencoder during the training process. Experimental results prove that our architecture CPGAN can maintain the quality of generated images and reduce at least 20% parameters and flops.

中文翻译：

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CPGAN：一种基于规范多Adadic分解的文本到图像生成对抗网络的高效架构设计

文本到图像的合成是计算机视觉的重要且具有挑战性的应用。提出了许多有趣且有意义的文本到图像合成模型。但是，大多数工作都关注合成图像的质量，但很少考虑这些模型的大小。大型模型包含许多参数和高延迟，这使其难以部署在移动应用程序上。为了解决这个问题，我们提出了一种基于规范多Adadic分解（CPD）的文本到图像生成对抗网络（GAN）的有效架构CPGAN。设计文本到图像GAN的轻量级体系结构是一种通用方法。为了提高CPGAN的稳定性，我们在训练过程中引入了条件增强和自动编码器的思想。