  Ensemble learning is a general technique to improve accuracy in machine
learning. However, the heavy computation of a ConvNets ensemble limits its
usage in deep learning. In this paper, we present Group Ensemble Network
(GENet), an architecture incorporating an ensemble of ConvNets in a single
ConvNet. Through a shared-base and multi-head structure, GENet is divided into
several groups to make explicit ensemble learning possible in a single ConvNet.
Owing to group convolution and the shared-base, GENet can fully leverage the
advantage of explicit ensemble learning while retaining the same computation as
a single ConvNet. Additionally, we present Group Averaging, Group Wagging and
Group Boosting as three different strategies to aggregate these ensemble
members. Finally, GENet outperforms larger single networks, standard ensembles
of smaller networks, and other recent state-of-the-art methods on CIFAR and
ImageNet. Specifically, group ensemble reduces the top-1 error by 1.83% for
ResNeXt-50 on ImageNet. We also demonstrate its effectiveness on action
recognition and object detection tasks.
