Abstract
Style transfer describes the rendering of an image semantic content as different artistic styles. Recently, generative adversarial networks (GANs) have emerged as an effective approach in style transfer by adversarially training the generator to synthesize convincing counterfeits. However, traditional GAN suffers from the mode collapse issue, resulting in unstable training and making style transfer quality difficult to guarantee. In addition, the GAN generator is only compatible with one style, so a series of GANs must be trained to provide users with choices to transfer more than one kind of style. In this paper, we focus on tackling these challenges and limitations to improve style transfer. We propose adversarial gated networks (Gated GAN) to transfer multiple styles in a single model. The generative networks have three modules: an encoder, a gated transformer, and a decoder. Different styles can be achieved by passing input images through different branches of the gated transformer. To stabilize training, the encoder and decoder are combined as an autoencoder to reconstruct the input images. The discriminative networks are used to distinguish whether the input image is a stylized or genuine image. An auxiliary classifier is used to recognize the style categories of transferred images, thereby helping the generative networks generate images in multiple styles. In addition, Gated GAN makes it possible to explore a new style by investigating styles learned from artists or genres. Our extensive experiments demonstrate the stability and effectiveness of the proposed model for multistyle transfer.
Abstract (translated)
样式转换将图像语义内容的呈现描述为不同的艺术样式。近年来，生成性对抗网络（gans）已成为一种有效的风格转换方法，通过对抗性地训练生成器合成令人信服的假冒产品。然而，传统的赣语在训练中存在着模式崩溃问题，训练不稳定，风格转换质量难以保证。此外，GAN生成器只与一种样式兼容，因此必须对一系列的GAN进行培训，以便为用户提供传递多种样式的选择。在本文中，我们将重点解决这些挑战和局限性，以改进样式转换。我们提出了一种对抗性门控网络（gated gan），在一个模型中传输多种样式。生成网络有三个模块：编码器、选通变压器和解码器。通过将输入图像通过选通变压器的不同分支，可以实现不同的样式。为了稳定训练，编码器和解码器结合为一个自动编码器来重建输入图像。识别网络用于区分输入图像是风格化图像还是真实图像。辅助分类器用于识别传输图像的样式类别，从而帮助生成网络生成多种样式的图像。此外，门控甘使探索一个新的风格，通过调查从艺术家或流派学习的风格可能。我们的大量实验证明了该模型的稳定性和有效性。
URL
https://arxiv.org/abs/1904.02296
