  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.
