Controlling Neural Style Transfer with Deep Reinforcement Learning
Chengming Feng Jing Hu Xin Wang Shu Hu Bin Zhu Xi Wu Hongtu Zhu Siwei Lyu
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.