Awesome Pruning

A curated list of neural network pruning and related resources. Inspired by awesome-deep-vision, awesome-adversarial-machine-learning, awesome-deep-learning-papers and Awesome-NAS.

Please feel free to pull requests or open an issue to add papers.

Type of Pruning
2019 Venues
2018 Venues
2017 Venues
2016 Venues
2015 Venues

Type of Pruning

Type	`F`	`W`	`Other`
Explanation	Filter pruning	Weight pruning	other types

2019

Title	Venue	Type	Code
Network Pruning via Transformable Architecture Search	NeurIPS	`F`	github
Gate Decorator: Global Filter Pruning Method for Accelerating Deep Convolutional Neural Networks	NeurIPS	`F`	github
Deconstructing Lottery Tickets: Zeros, Signs, and the Supermask	NeurIPS	`W`	github
One ticket to win them all: generalizing lottery ticket initializations across datasets and optimizers	NeurIPS	`W`	-
Global Sparse Momentum SGD for Pruning Very Deep Neural Networks	NeurIPS	`W`	github
AutoPrune: Automatic Network Pruning by Regularizing Auxiliary Parameters	NeurIPS	`W`	-
Model Compression with Adversarial Robustness: A Unified Optimization Framework	NeurIPS	`Other`	github
MetaPruning: Meta Learning for Automatic Neural Network Channel Pruning	ICCV	`F`	github
Accelerate CNN via Recursive Bayesian Pruning	ICCV	`F`	github
Adversarial Robustness vs Model Compression, or Both?	ICCV	`W`	github
Learning Filter Basis for Convolutional Neural Network Compression	ICCV	`Other`	-
Filter Pruning via Geometric Median for Deep Convolutional Neural Networks Acceleration	CVPR (Oral)	`F`	github
Towards Optimal Structured CNN Pruning via Generative Adversarial Learning	CVPR	`F`	github
Centripetal SGD for Pruning Very Deep Convolutional Networks with Complicated Structure	CVPR	`F`	github
On Implicit Filter Level Sparsity in Convolutional Neural Networks, Extension1, Extension2	CVPR	`F`	github
Structured Pruning of Neural Networks with Budget-Aware Regularization	CVPR	`F`	-
Importance Estimation for Neural Network Pruning	CVPR	`F`	github
OICSR: Out-In-Channel Sparsity Regularization for Compact Deep Neural Networks	CVPR	`F`	-
Partial Order Pruning: for Best Speed/Accuracy Trade-off in Neural Architecture Search	CVPR	`Other`	github
Variational Convolutional Neural Network Pruning	CVPR	-	-
The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks	ICLR (Best)	`W`	github
Rethinking the Value of Network Pruning	ICLR	`F`	github
Dynamic Channel Pruning: Feature Boosting and Suppression	ICLR	`F`	github
SNIP: Single-shot Network Pruning based on Connection Sensitivity	ICLR	`F`	github
Dynamic Sparse Graph for Efficient Deep Learning	ICLR	`F`	github
Collaborative Channel Pruning for Deep Networks	ICML	`F`	-
Approximated Oracle Filter Pruning for Destructive CNN Width Optimization github	ICML	`F`	-
EigenDamage: Structured Pruning in the Kronecker-Factored Eigenbasis4	ICML	`W`	github

2018

Title	Venue	Type	Code
Rethinking the Smaller-Norm-Less-Informative Assumption in Channel Pruning of Convolution Layers	ICLR	`F`	github
To prune, or not to prune: exploring the efficacy of pruning for model compression	ICLR	`W`	-
Discrimination-aware Channel Pruning for Deep Neural Networks	NeurIPS	`F`	github
Frequency-Domain Dynamic Pruning for Convolutional Neural Networks	NeurIPS	`W`	-
Amc: Automl for model compression and acceleration on mobile devices	ECCV	`F`	github
Data-Driven Sparse Structure Selection for Deep Neural Networks	ECCV	`F`	github
Coreset-Based Neural Network Compression	ECCV	`F`	github
Constraint-Aware Deep Neural Network Compression	ECCV	`W`	github
A Systematic DNN Weight Pruning Framework using Alternating Direction Method of Multipliers	ECCV	`W`	github
PackNet: Adding Multiple Tasks to a Single Network by Iterative Pruning	CVPR	`F`	github
NISP: Pruning Networks using Neuron Importance Score Propagation	CVPR	`F`	-
CLIP-Q: Deep Network Compression Learning by In-Parallel Pruning-Quantization	CVPR	`W`	-
“Learning-Compression” Algorithms for Neural Net Pruning	CVPR	`W`	-
Soft Filter Pruning for Accelerating Deep Convolutional Neural Networks	IJCAI	`F`	github
Accelerating Convolutional Networks via Global & Dynamic Filter Pruning	IJCAI	`F`	-

2017

Title	Venue	Type	Code
Pruning Filters for Efficient ConvNets	ICLR	`F`	github
Pruning Convolutional Neural Networks for Resource Efficient Inference	ICLR	`F`	github
Net-Trim: Convex Pruning of Deep Neural Networks with Performance Guarantee	NeurIPS	`W`	github
Learning to Prune Deep Neural Networks via Layer-wise Optimal Brain Surgeon	NeurIPS	`W`	github
Runtime Neural Pruning	NeurIPS	`F`	-
Designing Energy-Efficient Convolutional Neural Networks using Energy-Aware Pruning	CVPR	`F`	-
ThiNet: A Filter Level Pruning Method for Deep Neural Network Compression	ICCV	`F`	github
Channel pruning for accelerating very deep neural networks	ICCV	`F`	github
Learning Efficient Convolutional Networks Through Network Slimming	ICCV	`F`	github

2016

Title	Venue	Type	Code
Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Coding	ICLR (Best)	`W`	github
Dynamic Network Surgery for Efficient DNNs	NeurIPS	`W`	github

2015

Title	Venue	Type	Code
Learning both Weights and Connections for Efficient Neural Networks	NeurIPS	`W`	github

EfficientDNNs

Embedded-Neural-Network

awesome-AutoML-and-Lightweight-Models

Model-Compression-Papers

knowledge-distillation-papers

Network-Speed-and-Compression

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Awesome Pruning

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Type of Pruning

2019

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2017

2016

2015

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