Skip to content
master
Switch branches/tags
Code

Latest commit

 

Git stats

Files

Permalink
Failed to load latest commit information.
Type
Name
Latest commit message
Commit time
 
 
 
 

Deep Network Interpolation [Paper] [Project Page]

Deep Network Interpolation for Continuous Imagery Effect Transition

By Xintao Wang, Ke Yu, Chao Dong, Xiaoou Tang, Chen Change Loy

BibTeX

@Article{wang2018dni,
    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},
    journal={arXiv preprint arXiv:1811.10515},
    year={2018}
}

The following is a YouTube video showing several DNI examples. See our paper or the project page for more applications.

Highlights

  1. We propose a simple yet universal approach - deep network interpolation, for smooth and continuous imagery effect transition without further training.
  2. Different from previous works operating in the feature space, we make an attempt to investigate the manipulation in the parameter space of neural networks.
  3. Our analyses show that learned filters for several related tasks exhibit continuous changes. We believe that it is worth exploiting the underlying correlations of learned filters to further extend the ability and practicality of existing models.

DNI

DNI is simple and can be implemented with several lines of codes.

alpha = 0.3  # interpolation coefficient
net_A = torch.load('path_to_net_A.pth')
net_B = torch.load('path_to_net_B.pth')
net_interp = OrderedDict()
for k, v_A in net_A.items():
  v_B = net_B[k]
  net_interp[k] = alpha * v_A + (1 - alpha) * v_B

About

CVPR19 - Deep Network Interpolation for Continuous Imagery Effect Transition

Resources

Releases

No releases published

Packages

No packages published