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.

本文介绍了一种名为Groupable ConvNet (GroupNet)的新型网络，通过新型的动态分组卷积（DGConv）操作学习端到端的组数，达到了优于传统ResNet和ResNeXt的准确性和计算复杂度。该方法具有统一的卷积表示，可执行许多现有卷积操作，是一个可微和灵活的操作，可以从训练数据中学习执行各种卷积操作。

可分组卷积神经网络实现可微学习通道分组