Adversarial image generation by combining content and style. Issue 14 (1st December 2019)

• Record Type: Journal Article
• Authors: Liu, Songyan; Zhao, Chaoyang; Gao, Yunze; Wang, Jinqiao; Tang, Ming
• Is Part Of: IET image processing. Volume 13:Issue 14(2019)
• Page Start: 2716
• Page End: 2723
• Publication Date: 2019-12-01
• Subjects: image recognition -- feature extraction -- learning (artificial intelligence) -- image texture; adversarial image generation -- unique style -- reference images -- style feature extraction module -- style specific image generation model -- double‐cycle training strategy -- natural‐content pairs -- input natural images -- style exchange -- style‐exchanged images -- licence‐plate image -- handbags images; Image processing -- Periodicals; 621.36705
• DOI: 10.1049/iet-ipr.2019.0103
• Languages: English
• ISSNs: 1751-9659

Abstract

Abstract : Images can be considered as the combination of two parts: the content and the style. The authors' approach can leverage this property by extracting a certain unique style from the reference images and combining it to generate images with new contents. With a well‐defined style feature extraction module, they propose a novel framework to generate images with various styles and the same content. To train the style specific image generation model efficiently, a double‐cycle training strategy is proposed: they input two natural‐content pairs simultaneously, extract their style features, and exchange them twice to obtain the reconstruction of the input natural images. What is more, they apply the triplet margin loss to the style feature extracted from the images before and after style exchange and an adversarial discriminator to force the style‐exchanged images to be real. They perform experiments on licence‐plate image, Chinese characters, and shoes or handbags images generating, obtain photo‐realistic results and remarkably improve the corresponding supervised recognition task.