Realistic Style-Transfer Generative Adversarial Network With a Weight-Sharing Strategy
Shixiong Zhu, Xiangfeng Luo, Liyan Ma, Shaorong Xie, Han Zhang · 2020
Style transfer aims to generate images by combining the style of one image and the content of another. Though valuable efforts have been made in generating high-quality style transferred images, the resulting images are far from the distribution of real images. This greatly limits the application of style transfer such as improving the diversity of training set in computer vision task. We find that the reason of style transfer failing to generate realistic images is lack of reference targets and neglect of preserving data distribution. To solve the problem, we propose a Style-transfer Generative Adversarial Network with a weight-sharing strategy to make the stylized images be resemblance to the real images. The experimental results demonstrate that the proposed method can generate images with satisfying style transfers and high visual quality. Moreover, we apply our stylized images to augment the training set of object detection task, and improve the average precision faithfully. We believe that our method can enhance the performance of style transfer on computer vision tasks.