  In recent years, deep neural networks have achieved high ac-curacy in the
field of image recognition. By inspired from human learning method, we propose
a semantic segmentation method using cooperative learning which shares the
information resembling a group learning. We use two same networks and paths for
sending feature maps between two networks. Two networks are trained
simultaneously. By sharing feature maps, one of two networks can obtain the
information that cannot be obtained by a single network. In addition, in order
to enhance the degree of cooperation, we propose two kinds of methods that
connect only the same layer and multiple layers. We evaluated our proposed idea
on two kinds of networks. One is Dual Attention Network (DANet) and the other
one is DeepLabv3+. The proposed method achieved better segmentation accuracy
than the conventional single network and ensemble of networks.
