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Awesome Pruning Awesome

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

Type F W S Other
Explanation Filter pruning Weight pruning Special Networks other types

A Survey of Structured Pruning (arXiv version and IEEE T-PAMI version)

Please cite our paper if it's helpful:

@article{he2024structured,
  author={He, Yang and Xiao, Lingao},
  journal={IEEE Transactions on Pattern Analysis and Machine Intelligence}, 
  title={Structured Pruning for Deep Convolutional Neural Networks: A Survey}, 
  year={2024},
  volume={46},
  number={5},
  pages={2900-2919},
  doi={10.1109/TPAMI.2023.3334614}}

The related papers are categorized as below: Structured Pruning Taxonomy

2023

Title Venue Type Code
Revisiting Pruning at Initialization Through the Lens of Ramanujan Graph ICLR W PyTorch(Author)(Releasing)
Unmasking the Lottery Ticket Hypothesis: What's Encoded in a Winning Ticket's Mask? ICLR W -
Bit-Pruning: A Sparse Multiplication-Less Dot-Product ICLR W Code Deleted
NTK-SAP: Improving neural network pruning by aligning training dynamics ICLR W -
A Unified Framework for Soft Threshold Pruning ICLR W PyTorch(Author)
CrAM: A Compression-Aware Minimizer ICLR W -
Trainability Preserving Neural Pruning ICLR F -
DFPC: Data flow driven pruning of coupled channels without data ICLR F PyTorch(Author)
TVSPrune - Pruning Non-discriminative filters via Total Variation separability of intermediate representations without fine tuning ICLR F PyTorch(Author)
HomoDistil: Homotopic Task-Agnostic Distillation of Pre-trained Transformers ICLR F -
MECTA: Memory-Economic Continual Test-Time Model Adaptation ICLR F -
DepthFL : Depthwise Federated Learning for Heterogeneous Clients ICLR F -
OTOv2: Automatic, Generic, User-Friendly ICLR F PyTorch(Author)
Over-parameterized Model Optimization with Polyak-Lojasiewicz Condition ICLR F -
Pruning Deep Neural Networks from a Sparsity Perspective ICLR WF PyTorch(Author)
Holistic Adversarially Robust Pruning ICLR WF -
How I Learned to Stop Worrying and Love Retraining ICLR WF PyTorch(Author)
Symmetric Pruning in Quantum Neural Networks ICLR S -
Rethinking Graph Lottery Tickets: Graph Sparsity Matters ICLR S -
Joint Edge-Model Sparse Learning is Provably Efficient for Graph Neural Networks ICLR S -
Searching Lottery Tickets in Graph Neural Networks: A Dual Perspective ICLR S -
Diffusion Models for Causal Discovery via Topological Ordering ICLR S -
A General Framework For Proving The Equivariant Strong Lottery Ticket Hypothesis ICLR Other -
Sparsity May Cry: Let Us Fail (Current) Sparse Neural Networks Together! ICLR Other -
Minimum Variance Unbiased N:M Sparsity for the Neural Gradients ICLR Other -

2022

Title Venue Type Code
Parameter-Efficient Masking Networks NeurIPS W PyTorch(Author)
"Lossless" Compression of Deep Neural Networks: A High-dimensional Neural Tangent Kernel Approach NeurIPS W PyTorch(Author)
Losses Can Be Blessings: Routing Self-Supervised Speech Representations Towards Efficient Multilingual and Multitask Speech Processing NeurIPS W PyTorch(Author)
Models Out of Line: A Fourier Lens on Distribution Shift Robustness NeurIPS W PyTorch(Author)
Robust Binary Models by Pruning Randomly-initialized Networks NeurIPS W PyTorch(Author)
Rare Gems: Finding Lottery Tickets at Initialization NeurIPS W PyTorch(Author)
Optimal Brain Compression: A Framework for Accurate Post-Training Quantization and Pruning NeurIPS W PyTorch(Author)
Pruning’s Effect on Generalization Through the Lens of Training and Regularization NeurIPS W -
Back Razor: Memory-Efficient Transfer Learning by Self-Sparsified Backpropagation NeurIPS W PyTorch(Author)
Analyzing Lottery Ticket Hypothesis from PAC-Bayesian Theory Perspective NeurIPS W -
Sparse Winning Tickets are Data-Efficient Image Recognizers NeurIPS W PyTorch(Author)
Lottery Tickets on a Data Diet: Finding Initializations with Sparse Trainable Networks NeurIPS W -
Weighted Mutual Learning with Diversity-Driven Model Compression NeurIPS F -
SInGE: Sparsity via Integrated Gradients Estimation of Neuron Relevance NeurIPS F -
Data-Efficient Structured Pruning via Submodular Optimization NeurIPS F PyTorch(Author)
Structural Pruning via Latency-Saliency Knapsack NeurIPS F PyTorch(Author)
Recall Distortion in Neural Network Pruning and the Undecayed Pruning Algorithm NeurIPS WF -
Pruning Neural Networks via Coresets and Convex Geometry: Towards No Assumptions NeurIPS WF -
Controlled Sparsity via Constrained Optimization or: How I Learned to Stop Tuning Penalties and Love Constraints NeurIPS WF PyTorch(Author)
Advancing Model Pruning via Bi-level Optimization NeurIPS WF PyTorch(Author)
Emergence of Hierarchical Layers in a Single Sheet of Self-Organizing Spiking Neurons NeurIPS S -
CryptoGCN: Fast and Scalable Homomorphically Encrypted Graph Convolutional Network Inference NeurIPS S PyTorch(Author)(Releasing)
Transform Once: Efficient Operator Learning in Frequency Domain NeurIPS Other PyTorch(Author)(Releasing)
Most Activation Functions Can Win the Lottery Without Excessive Depth NeurIPS Other PyTorch(Author)
Pruning has a disparate impact on model accuracy NeurIPS Other -
Model Preserving Compression for Neural Networks NeurIPS Other PyTorch(Author)
Prune Your Model Before Distill It ECCV W PyTorch(Author)
FedLTN: Federated Learning for Sparse and Personalized Lottery Ticket Networks ECCV W -
FairGRAPE: Fairness-Aware GRAdient Pruning mEthod for Face Attribute Classification ECCV F PyTorch(Author)
SuperTickets: Drawing Task-Agnostic Lottery Tickets from Supernets via Jointly Architecture Searching and Parameter Pruning ECCV F PyTorch(Author)
Ensemble Knowledge Guided Sub-network Search and Fine-Tuning for Filter Pruning ECCV F PyTorch(Author)
CPrune: Compiler-Informed Model Pruning for Efficient Target-Aware DNN Execution ECCV F PyTorch(Author)
Soft Masking for Cost-Constrained Channel Pruning ECCV F PyTorch(Author)
Filter Pruning via Feature Discrimination in Deep Neural Networks ECCV F -
Disentangled Differentiable Network Pruning ECCV F -
Interpretations Steered Network Pruning via Amortized Inferred Saliency Maps ECCV F PyTorch(Author)
Bayesian Optimization with Clustering and Rollback for CNN Auto Pruning ECCV F PyTorch(Author)
Multi-granularity Pruning for Model Acceleration on Mobile Devices ECCV WF -
Exploring Lottery Ticket Hypothesis in Spiking Neural Networks ECCV S PyTorch(Author)
Towards Ultra Low Latency Spiking Neural Networks for Vision and Sequential Tasks Using Temporal Pruning ECCV S -
Recent Advances on Neural Network Pruning at Initialization IJCAI W PyTorch(Author)
FedDUAP: Federated Learning with Dynamic Update and Adaptive Pruning Using Shared Data on the Server IJCAI F -
On the Channel Pruning using Graph Convolution Network for Convolutional Neural Network Acceleration IJCAI F -
Pruning-as-Search: Efficient Neural Architecture Search via Channel Pruning and Structural Reparameterization IJCAI F -
Neural Network Pruning by Cooperative Coevolution IJCAI F -
SPDY: Accurate Pruning with Speedup Guarantees ICML W PyTorch(Author)
Sparse Double Descent: Where Network Pruning Aggravates Overfitting ICML W PyTorch(Author)
The Combinatorial Brain Surgeon: Pruning Weights That Cancel One Another in Neural Networks ICML W PyTorch(Author)
Linearity Grafting: Relaxed Neuron Pruning Helps Certifiable Robustness ICML F PyTorch(Author)
Winning the Lottery Ahead of Time: Efficient Early Network Pruning ICML F PyTorch(Author)
Topology-Aware Network Pruning using Multi-stage Graph Embedding and Reinforcement Learning ICML F PyTorch(Author)
Fast Lossless Neural Compression with Integer-Only Discrete Flows ICML F PyTorch(Author)
DepthShrinker: A New Compression Paradigm Towards Boosting Real-Hardware Efficiency of Compact Neural Networks ICML Other PyTorch(Author)
PAC-Net: A Model Pruning Approach to Inductive Transfer Learning ICML Other -
Neural Network Pruning Denoises the Features and Makes Local Connectivity Emerge in Visual Tasks ICML Other PyTorch(Author)
Interspace Pruning: Using Adaptive Filter Representations To Improve Training of Sparse CNNs CVPR W -
Masking Adversarial Damage: Finding Adversarial Saliency for Robust and Sparse Network CVPR W -
When To Prune? A Policy Towards Early Structural Pruning CVPR F -
Fire Together Wire Together: A Dynamic Pruning Approach With Self-Supervised Mask PredictionFire Together Wire Together: A Dynamic Pruning Approach With Self-Supervised Mask Prediction CVPR F -
Revisiting Random Channel Pruning for Neural Network Compression CVPR F PyTorch(Author)(Releasing)
Learning Bayesian Sparse Networks With Full Experience Replay for Continual Learning CVPR F -
DECORE: Deep Compression With Reinforcement Learning CVPR F -
CHEX: CHannel EXploration for CNN Model Compression CVPR F -
Compressing Models With Few Samples: Mimicking Then Replacing CVPR F PyTorch(Author)(Releasing)
Contrastive Dual Gating: Learning Sparse Features With Contrastive Learning CVPR WF -
DiSparse: Disentangled Sparsification for Multitask Model Compression CVPR Other PyTorch(Author)
Learning Pruning-Friendly Networks via Frank-Wolfe: One-Shot, Any-Sparsity, And No Retraining ICLR (Spotlight) W PyTorch(Author)
On Lottery Tickets and Minimal Task Representations in Deep Reinforcement Learning ICLR (Spotlight) W -
An Operator Theoretic View On Pruning Deep Neural Networks ICLR W PyTorch(Author)
Effective Model Sparsification by Scheduled Grow-and-Prune Methods ICLR W PyTorch(Author)
Signing the Supermask: Keep, Hide, Invert ICLR W -
How many degrees of freedom do we need to train deep networks: a loss landscape perspective ICLR W PyTorch(Author)
Dual Lottery Ticket Hypothesis ICLR W PyTorch(Author)
Peek-a-Boo: What (More) is Disguised in a Randomly Weighted Neural Network, and How to Find It Efficiently ICLR W PyTorch(Author)
Sparsity Winning Twice: Better Robust Generalization from More Efficient Training ICLR W PyTorch(Author)
SOSP: Efficiently Capturing Global Correlations by Second-Order Structured Pruning ICLR (Spotlight) F PyTorch(Author)(Releasing)
Pixelated Butterfly: Simple and Efficient Sparse training for Neural Network Models ICLR (Spotlight) F PyTorch(Author)
Revisit Kernel Pruning with Lottery Regulated Grouped Convolutions ICLR F PyTorch(Author)
Plant 'n' Seek: Can You Find the Winning Ticket? ICLR F PyTorch(Author)
Proving the Lottery Ticket Hypothesis for Convolutional Neural Networks ICLR F PyTorch(Author)
On the Existence of Universal Lottery Tickets ICLR F PyTorch(Author)
Training Structured Neural Networks Through Manifold Identification and Variance Reduction ICLR F PyTorch(Author)
Learning Efficient Image Super-Resolution Networks via Structure-Regularized Pruning ICLR F PyTorch(Author)
Prospect Pruning: Finding Trainable Weights at Initialization using Meta-Gradients ICLR WF PyTorch(Author)
The Unreasonable Effectiveness of Random Pruning: Return of the Most Naive Baseline for Sparse Training ICLR Other PyTorch(Author)
Prune and Tune Ensembles: Low-Cost Ensemble Learning with Sparse Independent Subnetworks AAAI W -
Prior Gradient Mask Guided Pruning-Aware Fine-Tuning AAAI F -
Convolutional Neural Network Compression through Generalized Kronecker Product Decomposition AAAI Other -

2021

Title Venue Type Code
Validating the Lottery Ticket Hypothesis with Inertial Manifold Theory NeurIPS W -
The Elastic Lottery Ticket Hypothesis NeurIPS W PyTorch(Author)
Sanity Checks for Lottery Tickets: Does Your Winning Ticket Really Win the Jackpot? NeurIPS W PyTorch(Author)
Why Lottery Ticket Wins? A Theoretical Perspective of Sample Complexity on Sparse Neural Networks NeurIPS W -
You are caught stealing my winning lottery ticket! Making a lottery ticket claim its ownership NeurIPS W PyTorch(Author)
Pruning Randomly Initialized Neural Networks with Iterative Randomization NeurIPS W PyTorch(Author)
Sparse Training via Boosting Pruning Plasticity with Neuroregeneration NeurIPS W PyTorch(Author)
AC/DC: Alternating Compressed/DeCompressed Training of Deep Neural Networks NeurIPS W PyTorch(Author)
A Winning Hand: Compressing Deep Networks Can Improve Out-of-Distribution Robustness NeurIPS W PyTorch(Author)
Rethinking the Pruning Criteria for Convolutional Neural Network NeurIPS F -
Only Train Once: A One-Shot Neural Network Training And Pruning Framework NeurIPS F PyTorch(Author)
CHIP: CHannel Independence-based Pruning for Compact Neural Networks NeurIPS F PyTorch(Author)
RED : Looking for Redundancies for Data-FreeStructured Compression of Deep Neural Networks NeurIPS F -
Compressing Neural Networks: Towards Determining the Optimal Layer-wise Decomposition NeurIPS F PyTorch(Author)
Sparse Flows: Pruning Continuous-depth Models NeurIPS WF PyTorch(Author)
Scaling Up Exact Neural Network Compression by ReLU Stability NeurIPS S PyTorch(Author)
Discriminator in GAN Compression: A Generator-discriminator Cooperative Compression Scheme NeurIPS S PyTorch(Author)
Heavy Tails in SGD and Compressibility of Overparametrized Neural Networks NeurIPS Other PyTorch(Author)
ResRep: Lossless CNN Pruning via Decoupling Remembering and Forgetting ICCV F PyTorch(Author)
Achieving on-Mobile Real-Time Super-Resolution with Neural Architecture and Pruning Search ICCV F -
GDP: Stabilized Neural Network Pruning via Gates with Differentiable Polarization ICCV F -
Auto Graph Encoder-Decoder for Neural Network Pruning ICCV F -
Exploration and Estimation for Model Compression ICCV F -
Sub-Bit Neural Networks: Learning To Compress and Accelerate Binary Neural Networks ICCV Other PyTorch(Author)
On the Predictability of Pruning Across Scales ICML W -
A Probabilistic Approach to Neural Network Pruning ICML F -
Accelerate CNNs from Three Dimensions: A Comprehensive Pruning Framework ICML F -
Group Fisher Pruning for Practical Network Compression ICML F PyTorch(Author)
Towards Compact CNNs via Collaborative Compression CVPR F PyTorch(Author)
Permute, Quantize, and Fine-tune: Efficient Compression of Neural Networks CVPR F PyTorch(Author)
NPAS: A Compiler-aware Framework of Unified Network Pruning andArchitecture Search for Beyond Real-Time Mobile Acceleration CVPR F -
Network Pruning via Performance Maximization CVPR F -
Convolutional Neural Network Pruning with Structural Redundancy Reduction CVPR F -
Manifold Regularized Dynamic Network Pruning CVPR F -
Joint-DetNAS: Upgrade Your Detector with NAS, Pruning and Dynamic Distillation CVPR FO -
Content-Aware GAN Compression CVPR S PyTorch(Author)
Multi-Prize Lottery Ticket Hypothesis: Finding Accurate Binary Neural Networks by Pruning A Randomly Weighted Network ICLR W PyTorch(Author)
Layer-adaptive Sparsity for the Magnitude-based Pruning ICLR W PyTorch(Author)
Pruning Neural Networks at Initialization: Why Are We Missing the Mark? ICLR W -
Robust Pruning at Initialization ICLR W -
A Gradient Flow Framework For Analyzing Network Pruning ICLR F PyTorch(Author)
Neural Pruning via Growing Regularization ICLR F PyTorch(Author)
ChipNet: Budget-Aware Pruning with Heaviside Continuous Approximations ICLR F PyTorch(Author)
Network Pruning That Matters: A Case Study on Retraining Variants ICLR F PyTorch(Author)

2020

Title Venue Type Code
Optimal Lottery Tickets via Subset Sum: Logarithmic Over-Parameterization is Sufficient NeurIPS W -
Winning the Lottery with Continuous Sparsification NeurIPS W PyTorch(Author)
HYDRA: Pruning Adversarially Robust Neural Networks NeurIPS W PyTorch(Author)
Logarithmic Pruning is All You Need NeurIPS W -
Directional Pruning of Deep Neural Networks NeurIPS W -
Movement Pruning: Adaptive Sparsity by Fine-Tuning NeurIPS W PyTorch(Author)
Sanity-Checking Pruning Methods: Random Tickets can Win the Jackpot NeurIPS W PyTorch(Author)
Neuron Merging: Compensating for Pruned Neurons NeurIPS F PyTorch(Author)
Neuron-level Structured Pruning using Polarization Regularizer NeurIPS F PyTorch(Author)
SCOP: Scientific Control for Reliable Neural Network Pruning NeurIPS F PyTorch(Author)
Storage Efficient and Dynamic Flexible Runtime Channel Pruning via Deep Reinforcement Learning NeurIPS F -
The Generalization-Stability Tradeoff In Neural Network Pruning NeurIPS F PyTorch(Author)
Greedy Optimization Provably Wins the Lottery: Logarithmic Number of Winning Tickets is Enough NeurIPS WF -
Pruning Filter in Filter NeurIPS Other PyTorch(Author)
Position-based Scaled Gradient for Model Quantization and Pruning NeurIPS Other PyTorch(Author)
Bayesian Bits: Unifying Quantization and Pruning NeurIPS Other -
Pruning neural networks without any data by iteratively conserving synaptic flow NeurIPS Other PyTorch(Author)
Meta-Learning with Network Pruning ECCV W -
Accelerating CNN Training by Pruning Activation Gradients ECCV W -
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)
DA-NAS: Data Adapted Pruning for Efficient Neural Architecture Search S 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 -
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 -
Soft Threshold Weight Reparameterization for Learnable Sparsity ICML WF Pytorch(Author)
Structured Compression by Weight Encryption for Unstructured Pruning and Quantization CVPR W -
Automatic Neural Network Compression by Sparsity-Quantization Joint Learning: A Constrained Optimization-Based Approach CVPR W -
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 -
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 -
Learning Filter Pruning Criteria for Deep Convolutional Neural Networks Acceleration CVPR F -
APQ: Joint Search for Network Architecture, Pruning and Quantization Policy CVPR F -
Multi-Dimensional Pruning: A Unified Framework for Model Compression CVPR (Oral) WF -
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)
Data-Independent Neural Pruning via Coresets ICLR W -
Provable Filter Pruning for Efficient Neural Networks ICLR F -
Dynamic Model Pruning with Feedback ICLR WF -
Comparing Rewinding and Fine-tuning in Neural Network Pruning ICLR (Oral) WF TensorFlow(Author)
AutoCompress: An Automatic DNN Structured Pruning Framework for Ultra-High Compression Rates AAAI F -
Reborn filters: Pruning convolutional neural networks with limited data 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
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 -
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)
Model Compression with Adversarial Robustness: A Unified Optimization Framework NeurIPS Other PyTorch(Author)
Adversarial Robustness vs Model Compression, or Both? ICCV W PyTorch(Author)
MetaPruning: Meta Learning for Automatic Neural Network Channel Pruning ICCV F PyTorch(Author)
Accelerate CNN via Recursive Bayesian Pruning ICCV F -
Learning Filter Basis for Convolutional Neural Network Compression ICCV Other -
Co-Evolutionary Compression for Unpaired Image Translation ICCV S -
COP: Customized Deep Model Compression via Regularized Correlation-Based Filter-Level Pruning IJCAI F Tensorflow(Author)
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 -
Variational Convolutional Neural Network Pruning CVPR F -
Partial Order Pruning: for Best Speed/Accuracy Trade-off in Neural Architecture Search CVPR Other TensorFlow(Author)
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 Eigenbasis ICML F PyTorch(Author)
The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks ICLR (Best) W TensorFlow(Author)
SNIP: Single-shot Network Pruning based on Connection Sensitivity ICLR W TensorFLow(Author)
Dynamic Channel Pruning: Feature Boosting and Suppression ICLR F TensorFlow(Author)
Rethinking the Value of Network Pruning ICLR F PyTorch(Author)
Dynamic Sparse Graph for Efficient Deep Learning ICLR F CUDA(3rd)

2018

Title Venue Type Code
Frequency-Domain Dynamic Pruning for Convolutional Neural Networks NeurIPS W -
Discrimination-aware Channel Pruning for Deep Neural Networks NeurIPS F TensorFlow(Author)
Learning Sparse Neural Networks via Sensitivity-Driven Regularization NeurIPS WF -
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)
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)
Soft Filter Pruning for Accelerating Deep Convolutional Neural Networks IJCAI F PyTorch(Author)
Accelerating Convolutional Networks via Global & Dynamic Filter Pruning IJCAI F -
Weightless: Lossy weight encoding for deep neural network compression ICML W -
Compressing Neural Networks using the Variational Information Bottleneck ICML F PyTorch(Author)
Deep k-Means: Re-Training and Parameter Sharing with Harder Cluster Assignments for Compressing Deep Convolutions ICML Other PyTorch(Author)
CLIP-Q: Deep Network Compression Learning by In-Parallel Pruning-Quantization CVPR W -
“Learning-Compression” Algorithms for Neural Net Pruning CVPR W -
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 -
To prune, or not to prune: exploring the efficacy of pruning for model compression ICLR W -
Rethinking the Smaller-Norm-Less-Informative Assumption in Channel Pruning of Convolution Layers ICLR F TensorFlow(Author), PyTorch(3rd)

2017

Title Venue Type Code
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 -
Structured Bayesian Pruning via Log-Normal Multiplicative Noise NeurIPS F -
Bayesian Compression for Deep Learning NeurIPS 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)
Variational Dropout Sparsifies Deep Neural Networks ICML W -
Combined Group and Exclusive Sparsity for Deep Neural Networks ICML WF -
Designing Energy-Efficient Convolutional Neural Networks using Energy-Aware Pruning CVPR W -
Pruning Filters for Efficient ConvNets ICLR F PyTorch(3rd)
Pruning Convolutional Neural Networks for Resource Efficient Inference ICLR F TensorFlow(3rd)

2016

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

2015

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

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A curated list of neural network pruning resources.

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