PyTorch implementation of Dynamic Grouping Convolution and Groupable ConvNet in Differentiable Learning-to-Group Channels via Groupable Convolutional Neural Networks.

• Kronecker Product is utilized to construct the sparse matrix efficiently and regularly.
• Discrete optimization is solved with the Straight-Through Estimator trick.
• Automatically learn the number of groups in an end-to-end differentiable fashion.

DGConv is used as a drop-in replacement of depthwise separable convolution in the original ResNeXt to build G-ResNeXt-50/101 network architectures. Here are some results of their performance comparison.

@InProceedings{Zhang_2019_ICCV,
author = {Zhang, Zhaoyang and Li, Jingyu and Shao, Wenqi and Peng, Zhanglin and Zhang, Ruimao and Wang, Xiaogang and Luo, Ping},
title = {Differentiable Learning-to-Group Channels via Groupable Convolutional Neural Networks},
booktitle = {The IEEE International Conference on Computer Vision (ICCV)},
month = {Oct},
year = {2019}
}