  In visual recognition, the key to the performance improvement of ResNet is
the success in establishing the stack of deep sequential convolutional layers
using identical mapping by a shortcut connection. It results in multiple paths
of data flow under a network and the paths are merged with the equal weights.
However, it is questionable whether it is correct to use the fixed and
predefined weights at the mapping units of all paths. In this paper, we
introduce the active weighted mapping method which infers proper weight values
based on the characteristic of input data on the fly. The weight values of each
mapping unit are not fixed but changed as the input image is changed, and the
most proper weight values for each mapping unit are derived according to the
input image. For this purpose, channel-wise information is embedded from both
the shortcut connection and convolutional block, and then the fully connected
layers are used to estimate the weight values for the mapping units. We train
the backbone network and the proposed module alternately for a more stable
learning of the proposed method. Results of the extensive experiments show that
the proposed method works successfully on the various backbone architectures
from ResNet to DenseNet. We also verify the superiority and generality of the
proposed method on various datasets in comparison with the baseline.
