v1v2v3 (latest)

Speeding-up Convolutional Neural Networks Using Fine-tuned CP-Decomposition

International Conference on Learning Representations (ICLR), 2014

19 December 2014

Papers citing "Speeding-up Convolutional Neural Networks Using Fine-tuned CP-Decomposition"

50 / 412 papers shown

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Reliable Identification of Redundant Kernels for Convolutional Neural Network Compression

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Efficient and Robust Machine Learning for Real-World Systems

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129

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Neural Rejuvenation: Improving Deep Network Training by Enhancing Computational Resource Utilization

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152

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Efficient Neural Network Compression

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Muhammad Umar Karim Khan

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ExpandNets: Linear Over-parameterization to Train Compact Convolutional Networks

Shuxuan Guo

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501

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Graph-Adaptive Pruning for Efficient Inference of Convolutional Neural Networks

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106

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Structured Pruning for Efficient ConvNets via Incremental RegularizationIEEE International Joint Conference on Neural Network (IJCNN), 2018

Huan Wang

Qiming Zhang

Yuehai Wang

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3DPC

248

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Convolutional Neural Networks with Transformed Input based on Robust Tensor Network Decomposition

137

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Compressing Recurrent Neural Networks with Tensor Ring for Action RecognitionAAAI Conference on Artificial Intelligence (AAAI), 2018

238

111

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Statistical Characteristics of Deep Representations: An Empirical Investigation

131

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Low-Rank Embedding of Kernels in Convolutional Neural Networks under Random Shuffling

106

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A Miniaturized Semantic Segmentation Method for Remote Sensing Image

Shou-Yu Chen

Guang-Sheng Chen

Wei-Peng Jing

27 Oct 2018

To Compress, or Not to Compress: Characterizing Deep Learning Model Compression for Embedded Inference

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177

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Rethinking the Value of Network Pruning

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301

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Penetrating the Fog: the Path to Efficient CNN Models

139

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Learning Recurrent Binary/Ternary WeightsInternational Conference on Learning Representations (ICLR), 2018

272

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ChannelNets: Compact and Efficient Convolutional Neural Networks via Channel-Wise Convolutions

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Extreme Network Compression via Filter Group Approximation

272

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A Unified Approximation Framework for Compressing and Accelerating Deep Neural Networks

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181

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LQ-Nets: Learned Quantization for Highly Accurate and Compact Deep Neural Networks

319

751

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162

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Recent Advances in Convolutional Neural Network Acceleration

200

372

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Filter Distillation for Network CompressionIEEE Workshop/Winter Conference on Applications of Computer Vision (WACV), 2018

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Luca Zappella

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285

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Auto Deep Compression by Reinforcement Learning Based Actor-Critic Structure

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144

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Impostor Networks for Fast Fine-Grained Recognition

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Artem Babenko

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126

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Perturbative Neural Networks

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Vishnu Boddeti

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137

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Constructing Fast Network through Deconstruction of Convolution

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303

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Jiashi Feng

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247

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Tensorial Neural Networks: Generalization of Neural Networks and Application to Model Compression

Jiahao Su

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106

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Faster Neural Network Training with Approximate Tensor Operations

257

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RotDCF: Decomposition of Convolutional Filters for Rotation-Equivariant Deep Networks

171

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159

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A Hierarchical Matcher using Local Classifier Chains

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A Support Tensor Train Machine

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Select, Attend, and Transfer: Light, Learnable Skip Connections

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224

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118

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163

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Diagonalwise Refactorization: An Efficient Training Method for Depthwise Convolutions

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126

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100

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Exploring Linear Relationship in Feature Map Subspace for ConvNets Compression

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150

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Wide Compression: Tensor Ring Nets

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186

181

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Loss-aware Weight Quantization of Deep Networks

Lu Hou

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246

135

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AMC: AutoML for Model Compression and Acceleration on Mobile Devices

Zhijian Liu

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463

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From Hashing to CNNs: Training BinaryWeight Networks via Hashing

Qinghao Hu

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208

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Universal Deep Neural Network Compression

Yoojin Choi

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261

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