ThiNet: Pruning CNN Filters for a Thinner Net
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Jianxin Wu | Hao Zhang | Weiyao Lin | Chen-Wei Xie | Jian-Hao Luo | Hong-Yu Zhou | Chen-Wei Xie | Jianxin Wu | Weiyao Lin | Hong-Yu Zhou | Jian-Hao Luo | Hao Zhang
[1] Wei Liu,et al. SSD: Single Shot MultiBox Detector , 2015, ECCV.
[2] Song Han,et al. Deep Compression: Compressing Deep Neural Network with Pruning, Trained Quantization and Huffman Coding , 2015, ICLR.
[3] Serge J. Belongie,et al. Arbitrary Style Transfer in Real-Time with Adaptive Instance Normalization , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).
[4] Qiang Chen,et al. Network In Network , 2013, ICLR.
[5] Alex Krizhevsky,et al. Learning Multiple Layers of Features from Tiny Images , 2009 .
[6] 한보형,et al. Learning Deconvolution Network for Semantic Segmentation , 2015 .
[7] Ali Farhadi,et al. YOLO9000: Better, Faster, Stronger , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[8] Leonidas J. Guibas,et al. Human action recognition by learning bases of action attributes and parts , 2011, 2011 International Conference on Computer Vision.
[9] Victor S. Lempitsky,et al. Fast ConvNets Using Group-Wise Brain Damage , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[10] Antonio Torralba,et al. Recognizing indoor scenes , 2009, CVPR.
[11] Zhiqiang Shen,et al. Learning Efficient Convolutional Networks through Network Slimming , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).
[12] Andrew Zisserman,et al. Very Deep Convolutional Networks for Large-Scale Image Recognition , 2014, ICLR.
[13] Song Han,et al. DSD: Dense-Sparse-Dense Training for Deep Neural Networks , 2016, ICLR.
[14] Iasonas Kokkinos,et al. DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs , 2016, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[15] Isaac Meilijson,et al. Synaptic Pruning in Development: A Computational Account , 1998, Neural Computation.
[16] Joan Bruna,et al. Exploiting Linear Structure Within Convolutional Networks for Efficient Evaluation , 2014, NIPS.
[17] Misha Denil,et al. Predicting Parameters in Deep Learning , 2014 .
[18] Ali Farhadi,et al. You Only Look Once: Unified, Real-Time Object Detection , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[19] Jianxin Wu,et al. ThiNet: A Filter Level Pruning Method for Deep Neural Network Compression , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).
[20] Song Han,et al. Learning both Weights and Connections for Efficient Neural Network , 2015, NIPS.
[21] Hanan Samet,et al. Pruning Filters for Efficient ConvNets , 2016, ICLR.
[22] Yu Wang,et al. Exploring the Granularity of Sparsity in Convolutional Neural Networks , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).
[23] Xiu-Shen Wei,et al. Selective Convolutional Descriptor Aggregation for Fine-Grained Image Retrieval , 2016, IEEE Transactions on Image Processing.
[24] Michael S. Bernstein,et al. ImageNet Large Scale Visual Recognition Challenge , 2014, International Journal of Computer Vision.
[25] Pietro Perona,et al. The Caltech-UCSD Birds-200-2011 Dataset , 2011 .
[26] Forrest N. Iandola,et al. SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <1MB model size , 2016, ArXiv.
[27] Yiran Chen,et al. Learning Structured Sparsity in Deep Neural Networks , 2016, NIPS.
[28] Rui Peng,et al. Network Trimming: A Data-Driven Neuron Pruning Approach towards Efficient Deep Architectures , 2016, ArXiv.
[29] Pascal Vincent,et al. Representation Learning: A Review and New Perspectives , 2012, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[30] Ross B. Girshick,et al. Fast R-CNN , 2015, 1504.08083.
[31] Bo Chen,et al. MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications , 2017, ArXiv.
[32] Ming Yang,et al. Compressing Deep Convolutional Networks using Vector Quantization , 2014, ArXiv.
[33] Luc Van Gool,et al. The Pascal Visual Object Classes Challenge: A Retrospective , 2014, International Journal of Computer Vision.
[34] Tara N. Sainath,et al. Structured Transforms for Small-Footprint Deep Learning , 2015, NIPS.
[35] Geoffrey E. Hinton,et al. ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.
[36] Yoshua Bengio,et al. Gradient-based learning applied to document recognition , 1998, Proc. IEEE.
[37] Trevor Darrell,et al. Caffe: Convolutional Architecture for Fast Feature Embedding , 2014, ACM Multimedia.
[38] Jian Cheng,et al. Quantized Convolutional Neural Networks for Mobile Devices , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[39] Timo Aila,et al. Pruning Convolutional Neural Networks for Resource Efficient Transfer Learning , 2016, ArXiv.
[40] Xiangyu Zhang,et al. Channel Pruning for Accelerating Very Deep Neural Networks , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).
[41] 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.
[42] Lior Wolf,et al. Channel-Level Acceleration of Deep Face Representations , 2015, IEEE Access.
[43] C. V. Jawahar,et al. Cats and dogs , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.
[44] Yixin Chen,et al. Compressing Neural Networks with the Hashing Trick , 2015, ICML.
[45] Dumitru Erhan,et al. Going deeper with convolutions , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[46] Bolei Zhou,et al. Learning Deep Features for Discriminative Localization , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[47] Pietro Perona,et al. Learning Generative Visual Models from Few Training Examples: An Incremental Bayesian Approach Tested on 101 Object Categories , 2004, 2004 Conference on Computer Vision and Pattern Recognition Workshop.
[48] Fei-Fei Li,et al. What, where and who? Classifying events by scene and object recognition , 2007, 2007 IEEE 11th International Conference on Computer Vision.
[49] Yann LeCun,et al. Optimal Brain Damage , 1989, NIPS.
[50] Nitish Srivastava,et al. Improving neural networks by preventing co-adaptation of feature detectors , 2012, ArXiv.
[51] Jian Sun,et al. Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[52] Geoffrey E. Hinton,et al. Learning distributed representations of concepts. , 1989 .