Gaoyuan Zhang, Songtao Lu, et al.
UAI 2022
We propose sparse-complementary convolution (SC-Conv) to improve model utilization of convolution neural networks (CNNs). The networks with SC-Conv achieve better accuracy than the regular convolution under similar computations and parameters. The proposed SC-Conv is paired with two deterministic sparse kernels, and one of kernels is complementary to the other one at in either spatial or channel domain or both; the deterministic sparsity increases the computational speed theoretically and practically; furthermore, by having the complementary characteristic, SC-Conv retains the same receptive field to the conventional convolution. This insightful but straightforward SC-Conv reuses of modern network architectures (ResNet and DenseNet), and at the same FLOPs and parameters, SC-Conv improves top-1 classification accuracy on ImageNet by 0.6 points for ResNet-101 and keep the same model complexity. Furthermore, SC-Conv also outperforms recent sparse networks by 1.3 points at top-1 accuracy for ImageNet, and after integrating SC-Conv with the sparse network, we further improve another 1.8 points accuracy at similar FLOPs and parameters.
Gaoyuan Zhang, Songtao Lu, et al.
UAI 2022
Quanfu Fan, Sharath Pankanti
AVSS 2011
Quanfu Fan, Lisa Brown, et al.
IV 2016
Sae Kyu Lee, Ankur Agrawal, et al.
IEEE JSSC