Icon Style Transformation Based on Generative Adversarial Networks
Authors: Hongyi Yang, Xinyue Wang, Chengqi Xue, Xiaoying Yang
Abstract: The icon is an important part of the user interface, and they are a carrier between the user and the interface. In the process of icon design, designers need to take into account both its versatility and uniqueness, and an excellent icon is a perfect blend of function and aesthetics. In recent years, with the great success of generative adversarial networks in computer vision, it has become possible to assist designers in icon creation with the help of artificial intelligence technology. In this study, we constructed icon datasets containing 40,000 samples and improved the structure and loss function based on the MUINT to finally achieve the style conversion task between different styles of icons. The research results show that the improved model can effectively improve the quality and diversity of generated icons. Meanwhile, a questionnaire survey of 34 people with icon design experience proves that our research results can assist designers to a certain extent in the related work. This study can be used as a basis for the intersection of deep generative model and icon design, and we conclude the paper with suggestions and prospects for future work.
Keywords: User Interface, Icon Design, Deep Learning, Generating Adversarial Network, Style Transfer
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