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.

翻译：神经艺术风格迁移（NST）模型能够通过添加著名图像的风格，改变简单图像的外观。尽管转换后的图像并不完全像相应风格图像艺术家创作的艺术品，但生成的图像颇具吸引力。通常，训练的NST模型专注于单一风格，且由单张图像代表该风格。然而，为新风格生成图像是一个繁琐的过程，需要完整的模型训练。本文提出了两种方法，旨在向独立于风格图像的神经风格迁移模型迈进。换言之，训练后的模型能够在任意内容与风格图像输入对下生成语义准确的生成图像。我们的创新贡献在于提出了一种单向生成对抗网络（unidirectional-GAN）模型，通过模型架构确保了循环一致性。此外，这导致模型尺寸显著缩小，并实现了高效的训练与验证阶段。