Structured Probabilistic Pruning for Convolutional Neural Network Acceleration

By Huan Wang, Qiming Zhang, Yuehai Wang, Haoji Hu. 2017

65 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

1. Yong Guo
2. Jing Liu
3. Mingkui Tan
4. Bohan Zhuang
5. Yun Raymond Fu
6. Yulun Zhang
7. Zhuangwei Zhuang
8. Junzhou Huang

Citations in the corpus

• Discrimination-Aware Network Pruning for Deep Model Compression (2020, methodology): “On ILSVRC-12, we compare DCP with SFP [21], GAL [41], Taylor-FO-BN [51], SPP [76], CP [24], GDP [40], FPGM [23], ThiNet [50], SSR-L2 [39], NetAdapt [79], AMC [22], and MetaPruning [46].”
• Neural Pruning via Growing Regularization (2020, background): “This phenomenon is broadly found by previous works Wen et al. (2016); Wang et al. (2018); He et al. (2017) but mainly on small datasets like CIFAR, while we make it on the much challenging ImageNet benchmark.”
• Balanced Sparsity for Efficient DNN Inference on GPU (2018, background): “( Wen et al. 2016; Wang et al. 2018) Therefore, customized hard-wares (Han et al. 2016; 2017; Parashar et al. 2017) are required for the widely deployment of model pruning.”