Efficient loss functions for GAN-based style transfer
Nhat-Tan Bui, Hai-Dang Nguyen, Trung-Nam Bui-Huynh, Ngoc-Thao Nguyen, Xuan-Nam Cao · 2023
Style transfer aims to render a new artistic image based on a content image and given artwork style. Recent style transfer techniques often suffer structure distortion and artifact problems that abate the quality of stylized images. Motivated by these observations and the previous works, we introduce a novel GAN framework to enhance the aesthetics, faithfulness and flexibility in the style transfer process. The key factor of our model is the Laplacian Pyramid loss that naturally forces the content preservation and the ResidualStyle discriminator block to capture the artwork’s painting style better. In contrast to existing methods that calculate the Euclidean distance between the features of generated image and content image, our Laplacian Pyramid loss better captures the content representation by different frequency bands of the content image. As evaluated by experimental results, our framework surmounts the unrealistic artifacts to synthesize the photorealistic artworks in real-time, hence attaining striking visual effects.