MetaPruning: Meta Learning for Automatic Neural Network Channel Pruning
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Xiangyu Zhang | Xin Yang | Kwang-Ting Cheng | Jian Sun | Zechun Liu | Zichao Guo | Haoyuan Mu | X. Zhang | Jian Sun | K. Cheng | Xin Yang | Zechun Liu | Zichao Guo | Haoyuan Mu | Xiangyu Zhang
[1] Yann LeCun,et al. Optimal Brain Damage , 1989, NIPS.
[2] Gregory J. Wolff,et al. Optimal Brain Surgeon and general network pruning , 1993, IEEE International Conference on Neural Networks.
[3] Li Fei-Fei,et al. ImageNet: A large-scale hierarchical image database , 2009, CVPR.
[4] P. Cochat,et al. Et al , 2008, Archives de pediatrie : organe officiel de la Societe francaise de pediatrie.
[5] Bogdan Gabrys,et al. Metalearning: a survey of trends and technologies , 2013, Artificial Intelligence Review.
[6] Misha Denil,et al. Predicting Parameters in Deep Learning , 2014 .
[7] Babak Saleh,et al. Write a Classifier: Zero-Shot Learning Using Purely Textual Descriptions , 2013, 2013 IEEE International Conference on Computer Vision.
[8] Song Han,et al. Learning both Weights and Connections for Efficient Neural Network , 2015, NIPS.
[9] Hassan Foroosh,et al. Sparse Convolutional Neural Networks , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[10] Andrew Zisserman,et al. Very Deep Convolutional Networks for Large-Scale Image Recognition , 2014, ICLR.
[11] Timo Aila,et al. Pruning Convolutional Neural Networks for Resource Efficient Transfer Learning , 2016, ArXiv.
[12] Mathieu Salzmann,et al. Learning the Number of Neurons in Deep Networks , 2016, NIPS.
[13] Wonyong Sung,et al. Compact Deep Convolutional Neural Networks With Coarse Pruning , 2016, ArXiv.
[14] Jian Sun,et al. Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[15] Suvrit Sra,et al. Diversity Networks , 2015, ICLR.
[16] Martial Hebert,et al. Learning to Learn: Model Regression Networks for Easy Small Sample Learning , 2016, ECCV.
[17] Rui Peng,et al. Network Trimming: A Data-Driven Neuron Pruning Approach towards Efficient Deep Architectures , 2016, ArXiv.
[18] Song Han,et al. Deep Compression: Compressing Deep Neural Network with Pruning, Trained Quantization and Huffman Coding , 2015, ICLR.
[19] Forrest N. Iandola,et al. SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <1MB model size , 2016, ArXiv.
[20] Ali Farhadi,et al. XNOR-Net: ImageNet Classification Using Binary Convolutional Neural Networks , 2016, ECCV.
[21] Yurong Chen,et al. Dynamic Network Surgery for Efficient DNNs , 2016, NIPS.
[22] Jianxin Wu,et al. ThiNet: A Filter Level Pruning Method for Deep Neural Network Compression , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).
[23] Hugo Larochelle,et al. Optimization as a Model for Few-Shot Learning , 2016, ICLR.
[24] Bo Chen,et al. MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications , 2017, ArXiv.
[25] Timo Aila,et al. Pruning Convolutional Neural Networks for Resource Efficient Inference , 2016, ICLR.
[26] Vivienne Sze,et al. Efficient Processing of Deep Neural Networks: A Tutorial and Survey , 2017, Proceedings of the IEEE.
[27] Alan L. Yuille,et al. Genetic CNN , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).
[28] Quoc V. Le,et al. HyperNetworks , 2016, ICLR.
[29] Quoc V. Le,et al. Neural Architecture Search with Reinforcement Learning , 2016, ICLR.
[30] Ramesh Raskar,et al. Designing Neural Network Architectures using Reinforcement Learning , 2016, ICLR.
[31] Wonyong Sung,et al. Structured Pruning of Deep Convolutional Neural Networks , 2015, ACM J. Emerg. Technol. Comput. Syst..
[32] Zhiqiang Shen,et al. Learning Efficient Convolutional Networks through Network Slimming , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).
[33] Hanan Samet,et al. Pruning Filters for Efficient ConvNets , 2016, ICLR.
[34] Xiangyu Zhang,et al. Channel Pruning for Accelerating Very Deep Neural Networks , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).
[35] Quoc V. Le,et al. Large-Scale Evolution of Image Classifiers , 2017, ICML.
[36] Song Han,et al. AMC: AutoML for Model Compression and Acceleration on Mobile Devices , 2018, ECCV.
[37] Naiyan Wang,et al. Data-Driven Sparse Structure Selection for Deep Neural Networks , 2017, ECCV.
[38] Quoc V. Le,et al. Understanding and Simplifying One-Shot Architecture Search , 2018, ICML.
[39] Wei Liu,et al. Bi-Real Net: Enhancing the Performance of 1-bit CNNs With Improved Representational Capability and Advanced Training Algorithm , 2018, ECCV.
[40] Yi Yang,et al. Soft Filter Pruning for Accelerating Deep Convolutional Neural Networks , 2018, IJCAI.
[41] Min Sun,et al. DPP-Net: Device-aware Progressive Search for Pareto-optimal Neural Architectures , 2018, ECCV.
[42] Xiangyu Zhang,et al. ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.
[43] Greg Mori,et al. Constraint-Aware Deep Neural Network Compression , 2018, ECCV.
[44] G. Hua,et al. LQ-Nets: Learned Quantization for Highly Accurate and Compact Deep Neural Networks , 2018, ECCV.
[45] Xiangyu Zhang,et al. ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design , 2018, ECCV.
[46] Yurong Chen,et al. Explicit Loss-Error-Aware Quantization for Low-Bit Deep Neural Networks , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.
[47] Trevor Darrell,et al. Learning to Segment Every Thing , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.
[48] Bo Chen,et al. NetAdapt: Platform-Aware Neural Network Adaptation for Mobile Applications , 2018, ECCV.
[49] James T. Kwok,et al. Loss-aware Weight Quantization of Deep Networks , 2018, ICLR.
[50] Mark Sandler,et al. MobileNetV2: Inverted Residuals and Linear Bottlenecks , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.
[51] Tong Yang,et al. MetaAnchor: Learning to Detect Objects with Customized Anchors , 2018, NeurIPS.
[52] Theodore Lim,et al. SMASH: One-Shot Model Architecture Search through HyperNetworks , 2017, ICLR.
[53] Quoc V. Le,et al. Efficient Neural Architecture Search via Parameter Sharing , 2018, ICML.
[54] Ning Xu,et al. Slimmable Neural Networks , 2018, ICLR.
[55] Yuandong Tian,et al. FBNet: Hardware-Aware Efficient ConvNet Design via Differentiable Neural Architecture Search , 2018, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[56] Ian D. Reid,et al. Structured Binary Neural Networks for Accurate Image Classification and Semantic Segmentation , 2018, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[57] Niraj K. Jha,et al. ChamNet: Towards Efficient Network Design Through Platform-Aware Model Adaptation , 2018, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[58] Tieniu Tan,et al. Meta-SR: A Magnification-Arbitrary Network for Super-Resolution , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[59] Yiming Yang,et al. DARTS: Differentiable Architecture Search , 2018, ICLR.
[60] Mingjie Sun,et al. Rethinking the Value of Network Pruning , 2018, ICLR.
[61] Song Han,et al. ProxylessNAS: Direct Neural Architecture Search on Target Task and Hardware , 2018, ICLR.