  Group convolution, which divides the channels of ConvNets into groups, has
achieved impressive improvement over the regular convolution operation.
However, existing models, eg. ResNeXt, still suffers from the sub-optimal
performance due to manually defining the number of groups as a constant over
all of the layers. Toward addressing this issue, we present Groupable ConvNet
(GroupNet) built by using a novel dynamic grouping convolution (DGConv)
operation, which is able to learn the number of groups in an end-to-end manner.
The proposed approach has several appealing benefits. (1) DGConv provides a
unified convolution representation and covers many existing convolution
operations such as regular dense convolution, group convolution, and depthwise
convolution. (2) DGConv is a differentiable and flexible operation which learns
to perform various convolutions from training data. (3) GroupNet trained with
DGConv learns different number of groups for different convolution layers.
Extensive experiments demonstrate that GroupNet outperforms its counterparts
such as ResNet and ResNeXt in terms of accuracy and computational complexity.
We also present introspection and reproducibility study, for the first time,
showing the learning dynamics of training group numbers.
