Deep Network Interpolation for Continuous Imagery Effect Transition

Xintao Wang, Ke Yu, Chao Dong, Xiaoou Tang, Chen Change Loy; The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2019, pp. 1692-1701

Abstract


Deep convolutional neural network has demonstrated its capability of learning a deterministic mapping for the desired imagery effect. However, the large variety of user flavors motivates the possibility of continuous transition among different output effects. Unlike existing methods that require a specific design to achieve one particular transition (e.g., style transfer), we propose a simple yet universal approach to attain a smooth control of diverse imagery effects in many low-level vision tasks, including image restoration, image-to-image translation, and style transfer. Specifically, our method, namely Deep Network Interpolation (DNI), applies linear interpolation in the parameter space of two or more correlated networks. A smooth control of imagery effects can be achieved by tweaking the interpolation coefficients. In addition to DNI and its broad applications, we also investigate the mechanism of network interpolation from the perspective of learned filters.

Related Material


[pdf]
[bibtex]
@InProceedings{Wang_2019_CVPR,
author = {Wang, Xintao and Yu, Ke and Dong, Chao and Tang, Xiaoou and Loy, Chen Change},
title = {Deep Network Interpolation for Continuous Imagery Effect Transition},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2019}
}