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arXiv · 2310.00405

Controlling Neural Style Transfer with Deep Reinforcement Learning

Chengming Feng·Jing Hu·Xin Wang·Shu Hu·Bin Zhu·Xi Wu·Hongtu Zhu·Siwei Lyu
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Abstract

Controlling the degree of stylization in the Neural Style Transfer (NST) is a little tricky since it usually needs hand-engineering on hyper-parameters. In this paper, we propose the first deep Reinforcement Learning (RL) based architecture that splits one-step style transfer into a step-wise process for the NST task. Our RL-based method tends to preserve more details and structures of the content image in early steps, and synthesize more style patterns in later steps. It is a user-easily-controlled style-transfer method. Additionally, as our RL-based model performs the stylization progressively, it is lightweight and has lower computational complexity than existing one-step Deep Learning (DL) based models. Experimental results demonstrate the effectiveness and robustness of our method.

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Chengming Feng, Jing Hu, Xin Wang, Shu Hu, Bin Zhu, Xi Wu, Hongtu Zhu, Siwei Lyu. 2023-09-30. Controlling Neural Style Transfer with Deep Reinforcement Learning. https://arxiv.org/abs/2310.00405

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