Convolution-Weight-Distribution Assumption: Rethinking the Criteria of Channel Pruning

By Zhongzhan Huang, Xinjiang Wang, Ping Luo. 2020

24 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. Zhongzhan Huang
2. Senwei Liang
3. Wei He
4. Mingfu Liang
5. Ming Liang
6. Meiqing Wu
7. Siew Kei Lam
8. Shaoshuai Shi

Citations in the corpus

• AlterSGD: Finding Flat Minima for Continual Learning by Alternative Training (2021, background): “Recently, deep neural networks (DNNs) became the dominant paradigm in various real-world applications, for example, image classification [12, 18, 17, 16, 19], semantic segmentation [43, 32, 27], object detection [39, 15, 38, 46, 35].”
• CAP: Context-Aware Pruning for Semantic Segmentation (2021, background): “Different pruning criteria are regarded as the indicators for redundant filters and have been studied [17, 26, 24], e.”