[综述]Deep Compression/Acceleration深度压缩/加速/量化

Model-Compression-Papers

Papers for neural network compression and acceleration. Partly based on link.

Survey

• Recent Advances in Efficient Computation of Deep Convolutional Neural Networks, [arxiv ‘18]

• A Survey of Model Compression and Acceleration for Deep Neural Networks [arXiv ‘17]

Quantization

• The ZipML Framework for Training Models with End-to-End Low Precision: The Cans, the Cannots, and a Little Bit of Deep Learning [ICML‘17]
• Compressing Deep Convolutional Networks using Vector Quantization [arXiv‘14]
• Quantized Convolutional Neural Networks for Mobile Devices [CVPR ‘16]
• Fixed-Point Performance Analysis of Recurrent Neural Networks [ICASSP‘16]
• Quantized Neural Networks: Training Neural Networks with Low Precision Weights and Activations [arXiv‘16]
• Loss-aware Binarization of Deep Networks [ICLR‘17]
• Towards the Limit of Network Quantization [ICLR‘17]
• Deep Learning with Low Precision by Half-wave Gaussian Quantization [CVPR‘17]
• ShiftCNN: Generalized Low-Precision Architecture for Inference of Convolutional Neural Networks [arXiv‘17]
• Training and Inference with Integers in Deep Neural Networks [ICLR‘18]
• Deep Learning with Limited Numerical Precision[ICML‘2015]
• Model compression via distillation and quantization [ICLR ‘18]
• Apprentice: Using Knowledge Distillation Techniques To Improve Low-Precision Network Accuracy [ICLR ‘18]
• On the Universal Approximability of Quantized ReLU Neural Networks [arXiv ‘18]
• Quantization and Training of Neural Networks for Efficient Integer-Arithmetic-Only Inference [CVPR ‘18]
• Binarized Neural Networks: Training Deep Neural Networks with Weights and Activations Constrained to +1 or -1 [NIPS ‘16]
• XNOR-Net: ImageNet Classification Using Binary Convolutional Neural Networks [ECCV ‘16]
• Binarized Convolutional Neural Networks with Separable Filters for Efficient Hardware Acceleration [CVPR ‘17]

Pruning

• Learning both Weights and Connections for Efficient Neural Networks [NIPS‘15]
• Pruning Filters for Efficient ConvNets [ICLR‘17]
• Pruning Convolutional Neural Networks for Resource Efficient Inference [ICLR‘17]
• Soft Weight-Sharing for Neural Network Compression [ICLR‘17]
• Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Coding [ICLR‘16]
• Dynamic Network Surgery for Efficient DNNs [NIPS‘16]
• Designing Energy-Efficient Convolutional Neural Networks using Energy-Aware Pruning [CVPR‘17]
• ThiNet: A Filter Level Pruning Method for Deep Neural Network Compression [ICCV‘17]
• To prune, or not to prune: exploring the efficacy of pruning for model compression [ICLR‘18]
• Data-Driven Sparse Structure Selection for Deep Neural Networks [arXiv ‘17]
• Learning Structured Sparsity in Deep Neural Networks [NIPS ‘16]
• Scalpel: Customizing DNN Pruning to the Underlying Hardware Parallelism [ISCA ‘17]
• Channel Pruning for Accelerating Very Deep Neural Networks [ICCV ‘17]
• Learning Efficient Convolutional Networks through Network Slimming [ICCV ‘17]
• NISP: Pruning Networks using Neuron Importance Score Propagation [CVPR ‘18]
• Rethinking the Smaller-Norm-Less-Informative Assumption in Channel Pruning of Convolution Layers [ICLR ‘18]
• MorphNet: Fast & Simple Resource-Constrained Structure Learning of Deep Networks [arXiv ‘17]
• Efficient Sparse-Winograd Convolutional Neural Networks [ICLR ‘18]

Low-rank Approximation

• Efficient and Accurate Approximations of Nonlinear Convolutional Networks [CVPR‘15]
• Accelerating Very Deep Convolutional Networks for Classification and Detection (Extended version of above one)
• Convolutional neural networks with low-rank regularization [arXiv‘15]
• Exploiting Linear Structure Within Convolutional Networks for Efficient Evaluation [NIPS‘14]
• Compression of Deep Convolutional Neural Networks for Fast and Low Power Mobile Applications [ICLR‘16]
• High performance ultra-low-precision convolutions on mobile devices [NIPS‘17]
• Speeding up convolutional neural networks with low rank expansions
• Coordinating Filters for Faster Deep Neural Networks [ICCV ‘17]

Knowledge Distillation

• Dark knowledge
• FitNets: Hints for Thin Deep Nets [ICLR ‘15]
• Net2net: Accelerating learning via knowledge transfer [ICLR ‘16]
• Distilling the Knowledge in a Neural Network [NIPS ‘15]
• MobileID: Face Model Compression by Distilling Knowledge from Neurons [AAAI ‘16]
• DarkRank: Accelerating Deep Metric Learning via Cross Sample Similarities Transfer [arXiv ‘17]
• Deep Model Compression: Distilling Knowledge from Noisy Teachers [arXiv ‘16]
• Paying More Attention to Attention: Improving the Performance of Convolutional Neural Networks via Attention Transfer [ICLR ‘17]
• Like What You Like: Knowledge Distill via Neuron Selectivity Transfer [arXiv ‘17]
• Learning Efficient Object Detection Models with Knowledge Distillation [NIPS ‘17]
• Data-Free Knowledge Distillation For Deep Neural Networks [NIPS ‘17]
• A Gift from Knowledge Distillation: Fast Optimization, Network Minimization and Transfer Learnin [CVPR ‘17]
• Moonshine: Distilling with Cheap Convolutions [arXiv ‘17]
• Model compression via distillation and quantization [ICLR ‘18]
• Apprentice: Using Knowledge Distillation Techniques To Improve Low-Precision Network Accuracy [ICLR ‘18]

Miscellaneous

• Beyond Filters: Compact Feature Map for Portable Deep Model [ICML ‘17]
• SplitNet: Learning to Semantically Split Deep Networks for Parameter Reduction and Model Parallelization [ICML ‘17]

[综述]Deep Compression/Acceleration深度压缩/加速/量化

原文：https://www.cnblogs.com/wildkid1024/p/10602925.html