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]
- Maxout Networks
- BinaryConnect: Training Deep Neural Networks with binary weights during propagations
- Ternary weight networks
- From Hashing to CNNs: Training Binary Weight Networks via Hashing
- Learning Accurate Low-Bit Deep Neural Networks with Stochastic Quantization
- TRAINED TERNARY QUANTIZATION
- DOREFA-NET: TRAINING LOW BITWIDTH CONVOLUTIONAL NEURAL NETWORKS WITH LOW BITWIDTH GRADIENTS
- Two-Step Quantization for Low-bit Neural Networks
- LQ-Nets: Learned Quantization for Highly Accurate and Compact Deep Neural Networks
- Fixed-point Factorized Networks
- INCREMENTAL NETWORK QUANTIZATION: TOWARDS LOSSLESS CNNS WITH LOW-PRECISION WEIGHTS
- Network Sketching: Exploiting Binary Structure in Deep CNNs
- Towards Effective Low-bitwidth Convolutional Neural Networks
- SYQ: Learning Symmetric Quantization For Efficient Deep Neural Networks
- Very deep convolutional networks for large-scale image recognition
- Towards Accurate Binary Convolutional Neural Network
- Estimating or Propagating Gradients Through Stochastic Neurons for Conditional Computation
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]
Reference
- [1] http://chenrudan.github.io/blog/2018/10/02/networkquantization.html
- [2] https://github.com/TerryLoveMl/Model-Compression-Papers
来源：oschina
链接：https://my.oschina.net/u/4307176/blog/3597240