  In Convolutional Neural Network (CNN) based image processing, most studies
propose networks that are optimized to single-level (or single-objective);
thus, they underperform on other levels and must be retrained for delivery of
optimal performance. Using multiple models to cover multiple levels involves
very high computational costs. To solve these problems, recent approaches train
networks on two different levels and propose their own interpolation methods to
enable arbitrary intermediate levels. However, many of them fail to generalize
or have certain side effects in practical usage. In this paper, we define these
frameworks as network tuning and interpolation and propose a novel module for
continuous-level learning, called Filter Transition Network (FTN). This module
is a structurally smoother module than existing ones. Therefore, the frameworks
with FTN generalize well across various tasks and networks and cause fewer
undesirable side effects. For stable learning of FTN, we additionally propose a
method to initialize non-linear neural network layers with identity mappings.
Extensive results for various image processing tasks indicate that the
performance of FTN is comparable in multiple continuous levels, and is
significantly smoother and lighter than that of other frameworks.
