ABSTRACT
Pruning can remove redundant parameters and structures of Deep Neural Networks (DNNs) to reduce inference time and memory overhead. As an important component of neural networks, the feature map (FM) has stated to be adopted for network pruning. However, the majority of FM-based pruning methods do not fully investigate effective knowledge in the FM for pruning. In addition, it is challenging to design a robust pruning criterion with a small number of images and achieve parallel pruning due to the variability of FMs. In this paper, we propose Adaptive Knowledge Extraction for Channel Pruning (AKECP), which can compress the network fast and efficiently. In AKECP, we first investigate the characteristics of FMs and extract effective knowledge with an adaptive scheme. Secondly, we formulate the effective knowledge of FMs to measure the importance of corresponding network channels. Thirdly, thanks to the effective knowledge extraction, AKECP can efficiently and simultaneously prune all the layers with extremely few or even one image. Experimental results show that our method can compress various networks on different datasets without introducing additional constraints, and it has advanced the state-of-the-arts. Notably, for ResNet-110 on CIFAR-10, AKECP achieves 59.9% of parameters and 59.8% of FLOPs reduction with negligible accuracy loss. For ResNet-50 on ImageNet, AKECP saves 40.5% of memory footprint and reduces 44.1% of FLOPs with only 0.32% of Top-1 accuracy drop.
Supplemental Material
- Haoli Bai, Jiaxiang Wu, Irwin King, and Michael Lyu. 2020. Few shot network compression via cross distillation. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 34. 3203--3210.Google ScholarCross Ref
- Wenlin Chen, James Wilson, Stephen Tyree, Kilian Weinberger, and Yixin Chen. 2015. Compressing neural networks with the hashing trick. In International conference on machine learning. PMLR, 2285--2294. Google ScholarDigital Library
- Emily Denton, Wojciech Zaremba, Joan Bruna, Yann LeCun, and Rob Fergus. 2014. Exploiting linear structure within convolutional networks for efficient evaluation. arXiv preprint arXiv:1404.0736 (2014).Google Scholar
- Minhao Fan, Wenjing Wang, Wenhan Yang, and Jiaying Liu. 2020. Integrating semantic segmentation and retinex model for low-light image enhancement. In Proceedings of the 28th ACM International Conference on Multimedia. 2317--2325. Google ScholarDigital Library
- Amir Gholami, Kiseok Kwon, Bichen Wu, Zizheng Tai, Xiangyu Yue, Peter Jin, Sicheng Zhao, and Kurt Keutzer. 2018. Squeezenext: Hardware-aware neural network design. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops. 1638--1647.Google ScholarCross Ref
- Zhangxuan Gu, Siyuan Zhou, Li Niu, Zihan Zhao, and Liqing Zhang. 2020. Context-aware Feature Generation for Zero-shot Semantic Segmentation. In Proceedings of the 28th ACM International Conference on Multimedia. 1921--1929. Google ScholarDigital Library
- Shaopeng Guo, Yujie Wang, Quanquan Li, and Junjie Yan. 2020. Dmcp: Differentiable markov channel pruning for neural networks. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 1539--1547.Google ScholarCross Ref
- Yiwen Guo, Anbang Yao, and Yurong Chen. 2016. Dynamic network surgery for efficient dnns. arXiv preprint arXiv:1608.04493 (2016). Google ScholarDigital Library
- Liang Han, Pichao Wang, Zhaozheng Yin, Fan Wang, and Hao Li. 2020. Exploiting Better Feature Aggregation for Video Object Detection. In Proceedings of the 28th ACM International Conference on Multimedia. 1469--1477. Google ScholarDigital Library
- Song Han, Huizi Mao, and William J Dally. 2015a. Deep compression: Compressing deep neural networks with pruning, trained quantization and huffman coding. arXiv preprint arXiv:1510.00149 (2015).Google Scholar
- Song Han, Jeff Pool, John Tran, and William J Dally. 2015b. Learning both weights and connections for efficient neural networks. arXiv preprint arXiv:1506.02626 (2015). Google ScholarDigital Library
- Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. 2016. Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition. 770--778.Google ScholarCross Ref
- Yang He, Guoliang Kang, Xuanyi Dong, Yanwei Fu, and Yi Yang. 2018. Soft filter pruning for accelerating deep convolutional neural networks. arXiv preprint arXiv:1808.06866 (2018). Google ScholarDigital Library
- Yang He, Ping Liu, Ziwei Wang, Zhilan Hu, and Yi Yang. 2019. Filter pruning via geometric median for deep convolutional neural networks acceleration. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 4340--4349.Google ScholarCross Ref
- Yihui He, Xiangyu Zhang, and Jian Sun. 2017. Channel pruning for accelerating very deep neural networks. In Proceedings of the IEEE International Conference on Computer Vision. 1389--1397.Google ScholarCross Ref
- Geoffrey Hinton, Oriol Vinyals, and Jeff Dean. 2015. Distilling the knowledge in a neural network. arXiv preprint arXiv:1503.02531 (2015).Google Scholar
- Andrew G Howard, Menglong Zhu, Bo Chen, Dmitry Kalenichenko, Weijun Wang, Tobias Weyand, Marco Andreetto, and Hartwig Adam. 2017. Mobilenets: Efficient convolutional neural networks for mobile vision applications. arXiv preprint arXiv:1704.04861 (2017).Google Scholar
- Hengyuan Hu, Rui Peng, Yu-Wing Tai, and Chi-Keung Tang. 2016. Network trimming: A data-driven neuron pruning approach towards efficient deep architectures. arXiv preprint arXiv:1607.03250 (2016).Google Scholar
- Gao Huang, Zhuang Liu, Laurens Van Der Maaten, and Kilian Q Weinberger. 2017. Densely connected convolutional networks. In Proceedings of the IEEE conference on computer vision and pattern recognition. 4700--4708.Google ScholarCross Ref
- Zehao Huang and Naiyan Wang. 2018. Data-driven sparse structure selection for deep neural networks. In Proceedings of the European conference on computer vision (ECCV). 304--320.Google ScholarDigital Library
- Minsoo Kang and Bohyung Han. 2020. Operation-aware soft channel pruning using differentiable masks. In International Conference on Machine Learning. PMLR, 5122--5131.Google Scholar
- Yong-Deok Kim, Eunhyeok Park, Sungjoo Yoo, Taelim Choi, Lu Yang, and Dongjun Shin. 2015. Compression of deep convolutional neural networks for fast and low power mobile applications. arXiv preprint arXiv:1511.06530 (2015).Google Scholar
- Alex Krizhevsky, Geoffrey Hinton, et al. 2009. Learning multiple layers of features from tiny images. (2009).Google Scholar
- Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton. 2012. Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems, Vol. 25 (2012), 1097--1105. Google ScholarDigital Library
- Se Jung Kwon, Dongsoo Lee, Byeongwook Kim, Parichay Kapoor, Baeseong Park, and Gu-Yeon Wei. 2020. Structured compression by weight encryption for unstructured pruning and quantization. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 1909--1918.Google ScholarCross Ref
- Yann LeCun, Léon Bottou, Yoshua Bengio, and Patrick Haffner. 1998. Gradient-based learning applied to document recognition. Proc. IEEE, Vol. 86, 11 (1998), 2278--2324.Google ScholarCross Ref
- Hao Li, Asim Kadav, Igor Durdanovic, Hanan Samet, and Hans Peter Graf. 2016. Pruning filters for efficient convnets. arXiv preprint arXiv:1608.08710 (2016).Google Scholar
- Yawei Li, Shuhang Gu, Christoph Mayer, Luc Van Gool, and Radu Timofte. 2020 a. Group sparsity: The hinge between filter pruning and decomposition for network compression. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 8018--8027.Google ScholarCross Ref
- Yawei Li, Shuhang Gu, Kai Zhang, Luc Van Gool, and Radu Timofte. 2020 b. Dhp: Differentiable meta pruning via hypernetworks. In Computer Vision--ECCV 2020: 16th European Conference, Glasgow, UK, August 23--28, 2020, Proceedings, Part VIII 16. Springer, 608--624.Google Scholar
- Yuchao Li, Shaohui Lin, Baochang Zhang, Jianzhuang Liu, David Doermann, Yongjian Wu, Feiyue Huang, and Rongrong Ji. 2019. Exploiting kernel sparsity and entropy for interpretable CNN compression. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2800--2809.Google ScholarCross Ref
- Mingbao Lin, Rongrong Ji, Yan Wang, Yichen Zhang, Baochang Zhang, Yonghong Tian, and Ling Shao. 2020. Hrank: Filter pruning using high-rank feature map. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 1529--1538.Google ScholarCross Ref
- Shaohui Lin, Rongrong Ji, Chenqian Yan, Baochang Zhang, Liujuan Cao, Qixiang Ye, Feiyue Huang, and David Doermann. 2019. Towards optimal structured cnn pruning via generative adversarial learning. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2790--2799.Google ScholarCross Ref
- Baoyuan Liu, Min Wang, Hassan Foroosh, Marshall Tappen, and Marianna Pensky. 2015. Sparse convolutional neural networks. In Proceedings of the IEEE conference on computer vision and pattern recognition. 806--814.Google Scholar
- Lizhao Liu, Junyi Cao, Minqian Liu, Yong Guo, Qi Chen, and Mingkui Tan. 2020. Dynamic Extension Nets for Few-shot Semantic Segmentation. In Proceedings of the 28th ACM International Conference on Multimedia. 1441--1449. Google ScholarDigital Library
- Zhuang Liu, Jianguo Li, Zhiqiang Shen, Gao Huang, Shoumeng Yan, and Changshui Zhang. 2017. Learning efficient convolutional networks through network slimming. In Proceedings of the IEEE International Conference on Computer Vision. 2736--2744.Google ScholarCross Ref
- Zechun Liu, Haoyuan Mu, Xiangyu Zhang, Zichao Guo, Xin Yang, Kwang-Ting Cheng, and Jian Sun. 2019. Metapruning: Meta learning for automatic neural network channel pruning. In Proceedings of the IEEE/CVF International Conference on Computer Vision. 3296--3305.Google ScholarCross Ref
- Jian-Hao Luo and Jianxin Wu. 2020. Neural Network Pruning with Residual-Connections and Limited-Data. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 1458--1467.Google ScholarCross Ref
- Jian-Hao Luo, Jianxin Wu, and Weiyao Lin. 2017. Thinet: A filter level pruning method for deep neural network compression. In Proceedings of the IEEE international conference on computer vision. 5058--5066.Google ScholarCross Ref
- Ningning Ma, Xiangyu Zhang, Hai-Tao Zheng, and Jian Sun. 2018. Shufflenet v2: Practical guidelines for efficient cnn architecture design. In Proceedings of the European conference on computer vision (ECCV). 116--131.Google ScholarDigital Library
- Mohammad Rastegari, Vicente Ordonez, Joseph Redmon, and Ali Farhadi. 2016. Xnor-net: Imagenet classification using binary convolutional neural networks. In European conference on computer vision. Springer, 525--542.Google ScholarCross Ref
- Olga Russakovsky, Jia Deng, Hao Su, Jonathan Krause, Sanjeev Satheesh, Sean Ma, Zhiheng Huang, Andrej Karpathy, Aditya Khosla, Michael Bernstein, et al. 2015. Imagenet large scale visual recognition challenge. International journal of computer vision, Vol. 115, 3 (2015), 211--252. Google ScholarDigital Library
- Xiruo Shi, Liutong Xu, Pengfei Wang, Yuanyuan Gao, Haifang Jian, and Wu Liu. 2020. Beyond the Attention: Distinguish the Discriminative and Confusable Features For Fine-grained Image Classification. In Proceedings of the 28th ACM International Conference on Multimedia. 601--609. Google ScholarDigital Library
- Xavier Suau, Luca Zappella, Vinay Palakkode, and Nicholas Apostoloff. 2018. Principal filter analysis for guided network compression. arXiv preprint arXiv:1807.10585, Vol. 2 (2018).Google Scholar
- Yehui Tang, Yunhe Wang, Yixing Xu, Dacheng Tao, Chunjing Xu, Chao Xu, and Chang Xu. 2020. Scop: Scientific control for reliable neural network pruning. arXiv preprint arXiv:2010.10732 (2020).Google Scholar
- Frederick Tung and Greg Mori. 2018. Clip-q: Deep network compression learning by in-parallel pruning-quantization. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 7873--7882.Google ScholarCross Ref
- Dong Wang, Lei Zhou, Xueni Zhang, Xiao Bai, and Jun Zhou. 2018. Exploring linear relationship in feature map subspace for convnets compression. arXiv preprint arXiv:1803.05729 (2018).Google Scholar
- Shijie Wang, Zhihui Wang, Haojie Li, and Wanli Ouyang. 2020. Category-specific Semantic Coherency Learning for Fine-grained Image Recognition. In Proceedings of the 28th ACM International Conference on Multimedia. 174--183. Google ScholarDigital Library
- Zhihui Wang, Shijie Wang, Pengbo Zhang, Haojie Li, Wei Zhong, and Jianjun Li. 2019. Weakly supervised fine-grained image classification via correlation-guided discriminative learning. In Proceedings of the 27th ACM International Conference on Multimedia. 1851--1860. Google ScholarDigital Library
- Wei Wen, Chunpeng Wu, Yandan Wang, Yiran Chen, and Hai Li. 2016. Learning structured sparsity in deep neural networks. arXiv preprint arXiv:1608.03665 (2016).Google Scholar
- Jialian Wu, Liangchen Song, Tiancai Wang, Qian Zhang, and Junsong Yuan. 2020 b. Forest r-cnn: Large-vocabulary long-tailed object detection and instance segmentation. In Proceedings of the 28th ACM International Conference on Multimedia. 1570--1578. Google ScholarDigital Library
- Xiangping Wu, Qingcai Chen, Wei Li, Yulun Xiao, and Baotian Hu. 2020 a. AdaHGNN: Adaptive Hypergraph Neural Networks for Multi-Label Image Classification. In Proceedings of the 28th ACM International Conference on Multimedia. 284--293. Google ScholarDigital Library
- Jingkang Yang, Weirong Chen, Litong Feng, Xiaopeng Yan, Huabin Zheng, and Wayne Zhang. 2020. Webly Supervised Image Classification with Metadata: Automatic Noisy Label Correction via Visual-Semantic Graph. In Proceedings of the 28th ACM International Conference on Multimedia. 83--91. Google ScholarDigital Library
- Ruichi Yu, Ang Li, Chun-Fu Chen, Jui-Hsin Lai, Vlad I Morariu, Xintong Han, Mingfei Gao, Ching-Yung Lin, and Larry S Davis. 2018. Nisp: Pruning networks using neuron importance score propagation. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 9194--9203.Google ScholarCross Ref
- Xiyu Yu, Tongliang Liu, Xinchao Wang, and Dacheng Tao. 2017. On compressing deep models by low rank and sparse decomposition. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 7370--7379.Google ScholarCross Ref
- Cai YuanQiang, Dawei Du, Libo Zhang, Longyin Wen, Weiqiang Wang, Yanjun Wu, and Siwei Lyu. 2020. Guided Attention Network for Object Detection and Counting on Drones. In Proceedings of the 28th ACM International Conference on Multimedia. 709--717. Google ScholarDigital Library
- Kaihua Zhang, Long Wang, Dong Liu, Bo Liu, Qingshan Liu, and Zhu Li. 2020. Dual Temporal Memory Network for Efficient Video Object Segmentation. In Proceedings of the 28th ACM International Conference on Multimedia. 1515--1523. Google ScholarDigital Library
- Chenglong Zhao, Bingbing Ni, Jian Zhang, Qiwei Zhao, Wenjun Zhang, and Qi Tian. 2019. Variational convolutional neural network pruning. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2780--2789.Google ScholarCross Ref
- Bolei Zhou, Aditya Khosla, Agata Lapedriza, Aude Oliva, and Antonio Torralba. 2016. Learning deep features for discriminative localization. In Proceedings of the IEEE conference on computer vision and pattern recognition. 2921--2929.Google ScholarCross Ref
Index Terms
- AKECP: Adaptive Knowledge Extraction from Feature Maps for Fast and Efficient Channel Pruning
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