A neural artistic style transformation (NST) model can modify the appearance
of a simple image by adding the style of a famous image. Even though the
transformed images do not look precisely like artworks by the same artist of
the respective style images, the generated images are appealing. Generally, a
trained NST model specialises in a style, and a single image represents that
style. However, generating an image under a new style is a tedious process,
which includes full model training. In this paper, we present two methods that
step toward the style image independent neural style transfer model. In other
words, the trained model could generate semantically accurate generated image
under any content, style image input pair. Our novel contribution is a
unidirectional-GAN model that ensures the Cyclic consistency by the model
architecture.Furthermore, this leads to much smaller model size and an
efficient training and validation phase.

本文提出了一种新的神经样式转换模型，它是基于无需相关风格图像，可用于任何内容和风格的输入形成语义准确的生成图像，规避了全模型训练的烦恼，并通过单向 GAN 模型的确保循环一致性的结构，实现了更高效的训练和验证阶段以及更小的模型尺寸。