[15] Shangqian Gao, Feihu Huang, Jian Pei, and Heng Huang. Discrete model compression with resource constraint for deep neural networks. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 1899-1908, 2020.
[16] Ross Girshick. Fast r-cnn. In IEEE International Conference on Computer Vision (ICCV), pages 1440-1448, 2015.
[17] Ross Girshick, Jeff Donahue, Trevor Darrell, and Jitendra Malik. Rich feature hierarchies for accurate object detection and semantic segmentation. In IEEE International Conference on Computer Vision (ICCV), pages 580-587, 2014.
[18] Shaopeng Guo, Yujie Wang, Quanquan Li, and Junjie Yan. Dmcp: Differentiable markov channel pruning for neural networks. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 1539-1547, 2020.
[19] Song Han, Jeff Pool, John Tran, and William Dally. Learning both weights and connections for efficient neural network. In Advances in Neural Information Processing Systems (NeurIPS), pages 1135-1143, 2015.
[20] Babak Hassibi and David Stork. Second order derivatives for network pruning: Optimal brain surgeon. Advances in neural information processing systems, 5, 1992.
[21] Kaiming He, Georgia Gkioxari, Piotr Dollar, and Ross Girshick. Mask r-cnn. In IEEE International Conference on Computer Vision (ICCV), 2017.
[22] Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Deep residual learning for image recognition. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 770-778, 2016.
[23] Yang He, Yuhang Ding, Ping Liu, Linchao Zhu, Hanwang Zhang, and Yi Yang. Learning filter pruning criteria for deep convolutional neural networks acceleration. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 2009–2018, 2020.
[24] Yang He, Guoliang Kang, Xuanyi Dong, Yanwei Fu, and Yi Yang. Soft filter pruning for accelerating deep convolutional neural networks. In International Joint Conference on Artificial Intelligence (IJCAI), pages 2234-2240, 2018.
[25] Yang He, Ping Liu, Ziwei Wang, Zhilan Hu, and Yi Yang. Filter pruning via geometric median for deep convolutional neural networks acceleration. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 4340-4349, 2019.
[26] Yihui He, Ji Lin, Zhijian Liu, Hanrui Wang, Li-Jia Li, and Song Han. Amc: Automl for model compression and acceleration on mobile devices. In Proceedings of the European conference on computer vision (ECCV), pages 784-800, 2018.
[27] Yihui He, Xiangyu Zhang, and Jian Sun. Channel pruning for accelerating very deep neural networks. In Proceedings of the IEEE international conference on computer vision, pages 1389-1397, 2017.
[28] Zejiang Hou and Sun-Yuan Kung. A feature-map discriminant perspective for pruning deep neural networks. arXiv preprint arXiv:2005.13796, 2020.
[29] Zejiang Hou, Minghai Qin, Fei Sun, Xiaolong Ma, Kun Yuan, Yi Xu, Yen-Kuang Chen, Rong Jin, Yuan Xie, and Sun-Yuan Kung. Chex: Channel exploration for cnn model compression. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 12287-12298, 2022.
[30] Andrew G Howard, Menglong Zhu, Bo Chen, Dmitry Kalenichenko, Weijun Wang, Tobias Weyand, Marco Andreetto, and Hartwig Adam. Mobilenets: Efficient convolutional neural networks for mobile vision applications. arXiv preprint arXiv:1704.04861, 2017.
[31] Zehao Huang and Naiyan Wang. Data-driven sparse structure selection for deep neural networks. Proceedings of the European Conference on Computer Vision, pages 304-320, 2018.