Model-Compression-Papers

June 19, 2021 · View on GitHub

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

Survey

Efficient Deep Learning: A Survey on Making Deep Learning Models Smaller, Faster, and Better, [arXiv '21]
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]

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]
“Learning-Compression” Algorithms for Neural Net Pruning [CVPR '18]

Binarized Neural Network

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