DPFPS: Dynamic and Progressive Filter Pruning for Compressing Convolutional Neural Networks from Scratch
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Weiming Hu | Bing Li | Yufan Liu | Xiaofeng Ruan | Chunfeng Yuan | Weiming Hu | Chunfen Yuan | Bing Li | Yufan Liu | Xiaofeng Ruan
[1] Song Han,et al. AMC: AutoML for Model Compression and Acceleration on Mobile Devices , 2018, ECCV.
[2] Yanzhi Wang,et al. A Systematic DNN Weight Pruning Framework using Alternating Direction Method of Multipliers , 2018, ECCV.
[3] Mingjie Sun,et al. Rethinking the Value of Network Pruning , 2018, ICLR.
[4] 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).
[5] Michael S. Bernstein,et al. ImageNet Large Scale Visual Recognition Challenge , 2014, International Journal of Computer Vision.
[6] Jian Sun,et al. Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[7] Chao Qian,et al. Efficient DNN Neuron Pruning by Minimizing Layer-wise Nonlinear Reconstruction Error , 2018, IJCAI.
[8] Ji Liu,et al. Global Sparse Momentum SGD for Pruning Very Deep Neural Networks , 2019, NeurIPS.
[9] Rongrong Ji,et al. HRank: Filter Pruning Using High-Rank Feature Map , 2020, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[10] M. Yuan,et al. Model selection and estimation in regression with grouped variables , 2006 .
[11] 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.
[12] Vinay P. Namboodiri,et al. Play and Prune: Adaptive Filter Pruning for Deep Model Compression , 2019, IJCAI.
[13] Andrew Zisserman,et al. Very Deep Convolutional Networks for Large-Scale Image Recognition , 2014, ICLR.
[14] Mark Sandler,et al. MobileNetV2: Inverted Residuals and Linear Bottlenecks , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.
[15] Hanan Samet,et al. Pruning Filters for Efficient ConvNets , 2016, ICLR.
[16] Xuelong Li,et al. Towards Compact ConvNets via Structure-Sparsity Regularized Filter Pruning , 2019, ArXiv.
[17] Chunfeng Yuan,et al. EDP: An Efficient Decomposition and Pruning Scheme for Convolutional Neural Network Compression , 2020, IEEE Transactions on Neural Networks and Learning Systems.
[18] Jiaxiang Wu,et al. Collaborative Channel Pruning for Deep Networks , 2019, ICML.
[19] Naiyan Wang,et al. Data-Driven Sparse Structure Selection for Deep Neural Networks , 2017, ECCV.
[20] Xiangyu Zhang,et al. Channel Pruning for Accelerating Very Deep Neural Networks , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).
[21] Jieping Ye,et al. AutoCompress: An Automatic DNN Structured Pruning Framework for Ultra-High Compression Rates , 2020, AAAI.
[22] Larry S. Davis,et al. NISP: Pruning Networks Using Neuron Importance Score Propagation , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.
[23] Stephen P. Boyd,et al. Convex Optimization , 2004, Algorithms and Theory of Computation Handbook.
[24] Miguel Á. Carreira-Perpiñán,et al. Low-Rank Compression of Neural Nets: Learning the Rank of Each Layer , 2020, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[25] Pavlo Molchanov,et al. Importance Estimation for Neural Network Pruning , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[26] Bing Li,et al. Knowledge Distillation via Instance Relationship Graph , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[27] Luc Van Gool,et al. The Pascal Visual Object Classes Challenge: A Retrospective , 2014, International Journal of Computer Vision.
[28] Song Han,et al. Learning both Weights and Connections for Efficient Neural Network , 2015, NIPS.
[29] Mathieu Salzmann,et al. Learning the Number of Neurons in Deep Networks , 2016, NIPS.
[30] Zhenyu Liao,et al. AdaBits: Neural Network Quantization With Adaptive Bit-Widths , 2020, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[31] Chenggang Yan,et al. Asymptotic Soft Filter Pruning for Deep Convolutional Neural Networks , 2020, IEEE Transactions on Cybernetics.
[32] Yi Yang,et al. Soft Filter Pruning for Accelerating Deep Convolutional Neural Networks , 2018, IJCAI.
[33] Geoffrey E. Hinton,et al. ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.
[34] Jungong Han,et al. Approximated Oracle Filter Pruning for Destructive CNN Width Optimization , 2019, ICML.
[35] Jianxin Wu,et al. ThiNet: Pruning CNN Filters for a Thinner Net , 2019, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[36] Jianxin Wu,et al. ThiNet: A Filter Level Pruning Method for Deep Neural Network Compression , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).
[37] Luca Bertinetto,et al. Fully-Convolutional Siamese Networks for Object Tracking , 2016, ECCV Workshops.
[38] Jingyu Wang,et al. OICSR: Out-In-Channel Sparsity Regularization for Compact Deep Neural Networks , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[39] Yiran Chen,et al. Learning Structured Sparsity in Deep Neural Networks , 2016, NIPS.
[40] Alex Krizhevsky,et al. Learning Multiple Layers of Features from Tiny Images , 2009 .
[41] Zhiqiang Shen,et al. Learning Efficient Convolutional Networks through Network Slimming , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).
[42] Luca Antiga,et al. Automatic differentiation in PyTorch , 2017 .
[43] Trevor Darrell,et al. Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation , 2013, 2014 IEEE Conference on Computer Vision and Pattern Recognition.