Stylised Image Generation From Deep Neural Networks

The purpose of convolutional neural networks is usually image classification but there are increasing studies attempting to reverse this common purpose in order to generate images. One of the most promising research directions is style transfer. This involves rendering the overall texture of an image into an artistic style. There are two common approaches in this field, which are feature representation based methods and generative adversarial network(GAN) based methods. In this paper, we focus on GAN based methods. We observed that most variants of GAN usually need paired data in order to generate the desired result, the training costs are very heavy and the quality of the result is not guaranteed. We propose an improved architecture for generative adversarial models for multi-style rendering. A new loss function configuration enables learning from unpaired data and generation of stylized images with specific artistic styles from normal photographs. A weighted combination of loss functions can control the trade-off between style and content of a stylized image.