DivSwapper: Towards Diversified Patch-based Arbitrary Style Transfer

DivSwapper: Towards Diversified Patch-based Arbitrary Style Transfer

Zhizhong Wang, Lei Zhao, Haibo Chen, Zhiwen Zuo, Ailin Li, Wei Xing, Dongming Lu

Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence
AI and Arts. Pages 4980-4987. https://doi.org/10.24963/ijcai.2022/690

Gram-based and patch-based approaches are two important research lines of style transfer. Recent diversified Gram-based methods have been able to produce multiple and diverse stylized outputs for the same content and style images. However, as another widespread research interest, the diversity of patch-based methods remains challenging due to the stereotyped style swapping process based on nearest patch matching. To resolve this dilemma, in this paper, we dive into the crux of existing patch-based methods and propose a universal and efficient module, termed DivSwapper, for diversified patch-based arbitrary style transfer. The key insight is to use an essential intuition that neural patches with higher activation values could contribute more to diversity. Our DivSwapper is plug-and-play and can be easily integrated into existing patch-based and Gram-based methods to generate diverse results for arbitrary styles. We conduct theoretical analyses and extensive experiments to demonstrate the effectiveness of our method, and compared with state-of-the-art algorithms, it shows superiority in diversity, quality, and efficiency.
Keywords:
Application domains: Images and visual arts
Methods and resources: Machine learning, deep learning, neural models, reinforcement learning
Theory and philosophy of arts and creativity in AI systems: Autonomous creative or artistic AI