Unsupervised Generative Adversarial Network for Style Transfer using Multiple Discriminators

Style transfer is the rendering of the content image into the style of another image, such that the content image achieves the stylization of the style image. In this paper, we propose the cartoonlization style transfer method by using multiple discriminators in the Generative Adversarial Network (GAN). Cartoon images have specific characteristics which include black outlines, smooth surfaces, blurred effects that separate them from real images. We preprocessed the input images by Felzenszwalb segmentation algorithm and Gaussian kernel to extract the individual features from the input cartoon image dataset. In this work, we have introduced the usage of three discriminators for each such feature to help the generator in learning and generalizing the input data distribution. The generator network used in this work contains only 0.8M trainable parameters, which leads to less inference time. This approach shows that our method with the small size generator network can learn to generate superior results as compared to other methods. Qualitative and quantitative comparison with other methods demonstrated the superiority of our method.