EDP: An Efficient Decomposition and Pruning Scheme for Convolutional Neural Network Compression
暂无分享,去创建一个
Chunfeng Yuan | Bing Li | Weiming Hu | Stephen Maybank | Xiaofeng Ruan | Yufan Liu | Yangxi Li | S. Maybank | Weiming Hu | C. Yuan | Bing Li | Yangxi Li | Yufan Liu | Xiaofeng Ruan
[1] Sergey Ioffe,et al. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift , 2015, ICML.
[2] Ran El-Yaniv,et al. Binarized Neural Networks , 2016, NIPS.
[3] Larry S. Davis,et al. NISP: Pruning Networks Using Neuron Importance Score Propagation , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.
[4] Yi Yang,et al. Soft Filter Pruning for Accelerating Deep Convolutional Neural Networks , 2018, IJCAI.
[5] Mathieu Salzmann,et al. Compression-aware Training of Deep Networks , 2017, NIPS.
[6] Jing Liu,et al. Discrimination-aware Channel Pruning for Deep Neural Networks , 2018, NeurIPS.
[7] Kaiming He,et al. Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks , 2015, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[8] Yurong Chen,et al. Dynamic Network Surgery for Efficient DNNs , 2016, NIPS.
[9] Kilian Q. Weinberger,et al. Densely Connected Convolutional Networks , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[10] Luc Van Gool,et al. The Pascal Visual Object Classes Challenge: A Retrospective , 2014, International Journal of Computer Vision.
[11] Song Han,et al. Learning both Weights and Connections for Efficient Neural Network , 2015, NIPS.
[12] Chenggang Yan,et al. Asymptotic Soft Filter Pruning for Deep Convolutional Neural Networks , 2020, IEEE Transactions on Cybernetics.
[13] Xuelong Li,et al. Towards Compact ConvNets via Structure-Sparsity Regularized Filter Pruning , 2019, ArXiv.
[14] Philip H. S. Torr,et al. SNIP: Single-shot Network Pruning based on Connection Sensitivity , 2018, ICLR.
[15] Eunhyeok Park,et al. Compression of Deep Convolutional Neural Networks for Fast and Low Power Mobile Applications , 2015, ICLR.
[16] Luca Antiga,et al. Automatic differentiation in PyTorch , 2017 .
[17] Joan Bruna,et al. Exploiting Linear Structure Within Convolutional Networks for Efficient Evaluation , 2014, NIPS.
[18] Rongrong Ji,et al. ESPACE: Accelerating Convolutional Neural Networks via Eliminating Spatial and Channel Redundancy , 2017, AAAI.
[19] Yann LeCun,et al. Optimal Brain Damage , 1989, NIPS.
[20] Song Han,et al. AMC: AutoML for Model Compression and Acceleration on Mobile Devices , 2018, ECCV.
[21] Song Han,et al. Deep Compression: Compressing Deep Neural Network with Pruning, Trained Quantization and Huffman Coding , 2015, ICLR.
[22] Ran El-Yaniv,et al. Quantized Neural Networks: Training Neural Networks with Low Precision Weights and Activations , 2016, J. Mach. Learn. Res..
[23] Jian Pei,et al. Discrete Model Compression With Resource Constraint for Deep Neural Networks , 2020, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[24] Babak Hassibi,et al. Second Order Derivatives for Network Pruning: Optimal Brain Surgeon , 1992, NIPS.
[25] Ivan V. Oseledets,et al. Speeding-up Convolutional Neural Networks Using Fine-tuned CP-Decomposition , 2014, ICLR.
[26] Jungong Han,et al. Approximated Oracle Filter Pruning for Destructive CNN Width Optimization , 2019, ICML.
[27] Jianxin Wu,et al. ThiNet: A Filter Level Pruning Method for Deep Neural Network Compression , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).
[28] Trevor Darrell,et al. Sequence to Sequence -- Video to Text , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).
[29] Luca Bertinetto,et al. Fully-Convolutional Siamese Networks for Object Tracking , 2016, ECCV Workshops.
[30] Geoffrey E. Hinton,et al. ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.
[31] Yoshua Bengio,et al. BinaryConnect: Training Deep Neural Networks with binary weights during propagations , 2015, NIPS.
[32] Andrew Zisserman,et al. Speeding up Convolutional Neural Networks with Low Rank Expansions , 2014, BMVC.
[33] Xiangyu Zhang,et al. Channel Pruning for Accelerating Very Deep Neural Networks , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).
[34] Mingjie Sun,et al. Rethinking the Value of Network Pruning , 2018, ICLR.
[35] Michael S. Bernstein,et al. ImageNet Large Scale Visual Recognition Challenge , 2014, International Journal of Computer Vision.
[36] Jian Sun,et al. Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[37] Rongrong Ji,et al. HRank: Filter Pruning Using High-Rank Feature Map , 2020, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[38] Rongrong Ji,et al. Exploiting Kernel Sparsity and Entropy for Interpretable CNN Compression , 2018, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[39] Chao Qian,et al. Efficient DNN Neuron Pruning by Minimizing Layer-wise Nonlinear Reconstruction Error , 2018, IJCAI.
[40] Ji Liu,et al. Global Sparse Momentum SGD for Pruning Very Deep Neural Networks , 2019, NeurIPS.
[41] Mark Sandler,et al. MobileNetV2: Inverted Residuals and Linear Bottlenecks , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.
[42] Hanan Samet,et al. Pruning Filters for Efficient ConvNets , 2016, ICLR.
[43] Kilian Q. Weinberger,et al. CondenseNet: An Efficient DenseNet Using Learned Group Convolutions , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.
[44] Andrew Zisserman,et al. Very Deep Convolutional Networks for Large-Scale Image Recognition , 2014, ICLR.
[45] Cong Xu,et al. Coordinating Filters for Faster Deep Neural Networks , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).
[46] Song Han,et al. Trained Ternary Quantization , 2016, ICLR.
[47] Anders Krogh,et al. A Simple Weight Decay Can Improve Generalization , 1991, NIPS.
[48] Michael Carbin,et al. The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks , 2018, ICLR.
[49] Trevor Darrell,et al. Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation , 2013, 2014 IEEE Conference on Computer Vision and Pattern Recognition.
[50] Mário A. T. Figueiredo,et al. Learning to Share: simultaneous parameter tying and Sparsification in Deep Learning , 2018, ICLR.
[51] Ali Farhadi,et al. XNOR-Net: ImageNet Classification Using Binary Convolutional Neural Networks , 2016, ECCV.
[52] Alex Krizhevsky,et al. Learning Multiple Layers of Features from Tiny Images , 2009 .
[53] Zhiqiang Shen,et al. Learning Efficient Convolutional Networks through Network Slimming , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).
[54] Jian Sun,et al. Accelerating Very Deep Convolutional Networks for Classification and Detection , 2015, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[55] Mark W. Schmidt,et al. Convergence Rates of Inexact Proximal-Gradient Methods for Convex Optimization , 2011, NIPS.
[56] Ping Liu,et al. Filter Pruning via Geometric Median for Deep Convolutional Neural Networks Acceleration , 2018, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[57] Marc Teboulle,et al. A Fast Iterative Shrinkage-Thresholding Algorithm for Linear Inverse Problems , 2009, SIAM J. Imaging Sci..