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.

Table of Contents

• Type of Pruning

• 2020 Venues

• 2019 Venues

• 2018 Venues

• 2017 Venues

• 2016 Venues

• 2015 Venues

Type of Pruning

Type	F	W	Other
Explanation	Filter pruning	Weight pruning	other types

2020

Title	Venue	Type	Code
EagleEye: Fast Sub-net Evaluation for Efficient Neural Network Pruning	ECCV (Oral)	F	PyTorch(Author)
DSA: More Efficient Budgeted Pruning via Differentiable Sparsity Allocation	ECCV	F	-
DHP: Differentiable Meta Pruning via HyperNetworks	ECCV	F	PyTorch(Author)
Meta-Learning with Network Pruning	ECCV	W	-
Accelerating CNN Training by Pruning Activation Gradients	ECCV	W	-
DA-NAS: Data Adapted Pruning for Efficient Neural Architecture Search	ECCV	Other	-
Differentiable Joint Pruning and Quantization for Hardware Efficiency	ECCV	Other	-
Channel Pruning via Automatic Structure Search	IJCAI	F	PyTorch(Author)
Adversarial Neural Pruning with Latent Vulnerability Suppression	ICML	W	-
Proving the Lottery Ticket Hypothesis: Pruning is All You Need	ICML	W	-
Soft Threshold Weight Reparameterization for Learnable Sparsity	ICML	WF	Pytorch(Author)
Network Pruning by Greedy Subnetwork Selection	ICML	F	-
Operation-Aware Soft Channel Pruning using Differentiable Masks	ICML	F	-
DropNet: Reducing Neural Network Complexity via Iterative Pruning	ICML	F	-
Towards Efficient Model Compression via Learned Global Ranking	CVPR (Oral)	F	Pytorch(Author)
HRank: Filter Pruning using High-Rank Feature Map	CVPR (Oral)	F	Pytorch(Author)
Neural Network Pruning with Residual-Connections and Limited-Data	CVPR (Oral)	F	-
Multi-Dimensional Pruning: A Unified Framework for Model Compression	CVPR (Oral)	WF	-
DMCP: Differentiable Markov Channel Pruning for Neural Networks	CVPR (Oral)	F	TensorFlow(Author)
Group Sparsity: The Hinge Between Filter Pruning and Decomposition for Network Compression	CVPR	F	PyTorch(Author)
Few Sample Knowledge Distillation for Efficient Network Compression	CVPR	F	-
Discrete Model Compression With Resource Constraint for Deep Neural Networks	CVPR	F	-
Structured Compression by Weight Encryption for Unstructured Pruning and Quantization	CVPR	W	-
Learning Filter Pruning Criteria for Deep Convolutional Neural Networks Acceleration	CVPR	F	-
APQ: Joint Search for Network Architecture, Pruning and Quantization Policy	CVPR	F	-
Comparing Rewinding and Fine-tuning in Neural Network Pruning	ICLR (Oral)	WF	TensorFlow(Author)
A Signal Propagation Perspective for Pruning Neural Networks at Initialization	ICLR (Spotlight)	W	-
ProxSGD: Training Structured Neural Networks under Regularization and Constraints	ICLR	W	TF+PT(Author)
One-Shot Pruning of Recurrent Neural Networks by Jacobian Spectrum Evaluation	ICLR	W	-
Lookahead: A Far-sighted Alternative of Magnitude-based Pruning	ICLR	W	PyTorch(Author)
Dynamic Model Pruning with Feedback	ICLR	WF	-
Provable Filter Pruning for Efficient Neural Networks	ICLR	F	-
Data-Independent Neural Pruning via Coresets	ICLR	W	-
AutoCompress: An Automatic DNN Structured Pruning Framework for Ultra-High Compression Rates	AAAI	F	-
DARB: A Density-Aware Regular-Block Pruning for Deep Neural Networks	AAAI	Other	-
Pruning from Scratch	AAAI	Other	-

2019

Title	Venue	Type	Code
Network Pruning via Transformable Architecture Search	NeurIPS	F	PyTorch(Author)
Gate Decorator: Global Filter Pruning Method for Accelerating Deep Convolutional Neural Networks	NeurIPS	F	PyTorch(Author)
Deconstructing Lottery Tickets: Zeros, Signs, and the Supermask	NeurIPS	W	TensorFlow(Author)
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	PyTorch(Author)
AutoPrune: Automatic Network Pruning by Regularizing Auxiliary Parameters	NeurIPS	W	-
Model Compression with Adversarial Robustness: A Unified Optimization Framework	NeurIPS	Other	PyTorch(Author)
MetaPruning: Meta Learning for Automatic Neural Network Channel Pruning	ICCV	F	PyTorch(Author)
Accelerate CNN via Recursive Bayesian Pruning	ICCV	F	-
Adversarial Robustness vs Model Compression, or Both?	ICCV	W	PyTorch(Author)
Learning Filter Basis for Convolutional Neural Network Compression	ICCV	Other	-
Filter Pruning via Geometric Median for Deep Convolutional Neural Networks Acceleration	CVPR (Oral)	F	PyTorch(Author)
Towards Optimal Structured CNN Pruning via Generative Adversarial Learning	CVPR	F	PyTorch(Author)
Centripetal SGD for Pruning Very Deep Convolutional Networks with Complicated Structure	CVPR	F	PyTorch(Author)
On Implicit Filter Level Sparsity in Convolutional Neural Networks, Extension1, Extension2	CVPR	F	PyTorch(Author)
Structured Pruning of Neural Networks with Budget-Aware Regularization	CVPR	F	-
Importance Estimation for Neural Network Pruning	CVPR	F	PyTorch(Author)
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	TensorFlow(Author)
Variational Convolutional Neural Network Pruning	CVPR	-	-
The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks	ICLR (Best)	W	TensorFlow(Author)
Rethinking the Value of Network Pruning	ICLR	F	PyTorch(Author)
Dynamic Channel Pruning: Feature Boosting and Suppression	ICLR	F	TensorFlow(Author)
SNIP: Single-shot Network Pruning based on Connection Sensitivity	ICLR	W	TensorFLow(Author)
Dynamic Sparse Graph for Efficient Deep Learning	ICLR	F	CUDA(3rd)
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	PyTorch(Author)

2018

Title	Venue	Type	Code
Rethinking the Smaller-Norm-Less-Informative Assumption in Channel Pruning of Convolution Layers	ICLR	F	TensorFlow(Author), PyTorch(3rd)
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	TensorFlow(Author)
Frequency-Domain Dynamic Pruning for Convolutional Neural Networks	NeurIPS	W	-
Learning Sparse Neural Networks via Sensitivity-Driven Regularization	NeurIPS	WF	-
Amc: Automl for model compression and acceleration on mobile devices	ECCV	F	TensorFlow(3rd)
Data-Driven Sparse Structure Selection for Deep Neural Networks	ECCV	F	MXNet(Author)
Coreset-Based Neural Network Compression	ECCV	F	PyTorch(Author)
Constraint-Aware Deep Neural Network Compression	ECCV	W	SkimCaffe(Author)
A Systematic DNN Weight Pruning Framework using Alternating Direction Method of Multipliers	ECCV	W	Caffe(Author)
PackNet: Adding Multiple Tasks to a Single Network by Iterative Pruning	CVPR	F	PyTorch(Author)
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	PyTorch(Author)
Accelerating Convolutional Networks via Global & Dynamic Filter Pruning	IJCAI	F	-

2017

Title	Venue	Type	Code
Pruning Filters for Efficient ConvNets	ICLR	F	PyTorch(3rd)
Pruning Convolutional Neural Networks for Resource Efficient Inference	ICLR	F	TensorFlow(3rd)
Net-Trim: Convex Pruning of Deep Neural Networks with Performance Guarantee	NeurIPS	W	TensorFlow(Author)
Learning to Prune Deep Neural Networks via Layer-wise Optimal Brain Surgeon	NeurIPS	W	PyTorch(Author)
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	Caffe(Author), PyTorch(3rd)
Channel pruning for accelerating very deep neural networks	ICCV	F	Caffe(Author)
Learning Efficient Convolutional Networks Through Network Slimming	ICCV	F	PyTorch(Author)

2016

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

2015

Title	Venue	Type	Code
Learning both Weights and Connections for Efficient Neural Networks	NeurIPS	W	PyTorch(3rd)

Related Repo

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