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Self-grouping Convolutional Neural Networks
[article]
2020
arXiv
pre-print
Although group convolution operators are increasingly used in deep convolutional neural networks to improve the computational efficiency and to reduce the number of parameters, most existing methods construct their group convolution architectures by a predefined partitioning of the filters of each convolutional layer into multiple regular filter groups with an equal spatial group size and data-independence, which prevents a full exploitation of their potential. To tackle this issue, we propose
arXiv:2009.13803v1
fatcat:njxhz5amcnbtjbcjyeqfz5pfpi