01 |
Towards Optimal Structured CNN Pruning via Generative Adversarial Learning |
CVPR |
F |
GAL |
PyTorch(Author) |
Image Classification |
2019 |
02 |
Winning the Lottery with Continuous Sparsification |
NeurIPS |
F |
CS |
PyTorch(Author) |
Image Classification |
2019 |
03 |
Centripetal SGD for Pruning Very Deep Convolutional Networks with Complicated Structure |
CVPR |
F |
C-SGD |
Tensorflow(Author) |
Image Classification |
2019 |
04 |
ThiNet: A Filter Level Pruning Method for Deep Neural Network Compression |
ICCV&TPAMI |
F |
ThiNet |
Caffe(Author), PyTorch(3rd) |
Image Classification |
2017&2019 |
05 |
Channel pruning for accelerating very deep neural networks |
ICCV |
C |
- |
Caffe(Author) |
Image Classification&Object Detection |
2017 |
06 |
NISP: Pruning Networks using Neuron Importance Score Propagation |
CVPR |
NC |
NISP |
- |
Image Classification |
2018 |
07 |
Pruning Convolutional Neural Networks for Resource Efficient Inference |
ICLR |
F |
- |
PyTorch |
Image Classification |
2017 |
08 |
Discrimination-aware Channel Pruning for Deep Neural Networks |
NeurIPS |
C |
DCP |
TensorFlow(Author) |
Image Classification |
2018 |
09 |
Gate Decorator: Global Filter Pruning Method for Accelerating Deep Convolutional Neural Networks |
NeurIPS |
F |
Gate Decorator |
PyTorch(Author) |
Image Classification&Semantic Segmentation |
2019 |
10 |
Pruning Filters for Efficient ConvNets |
ICLR |
F |
PFEC |
PyTorch(3rd) |
Image Classification |
2017 |
11 |
Neural Network Pruning with Residual-Connections and Limited-Data |
CVPR |
C |
CURL |
PyTorch(Author) |
Image Classification |
2020 |
12 |
HRank: Filter Pruning using High-Rank Feature Map |
CVPR |
F |
HRank |
Pytorch(Author) |
Image Classification |
2020 |
13 |
Importance Estimation for Neural Network Pruning |
CVPR |
F |
Taylor-FO-BN |
PyTorch(Author) |
Image Classification |
2019 |
14 |
Accelerate CNNs from Three Dimensions: A Comprehensive Pruning Framework |
ICML |
F |
- |
- |
Image Classification |
2021 |
15 |
Learning Filter Pruning Criteria for Deep Convolutional Neural Networks Acceleration |
CVPR |
F |
LFPC |
- |
Image Classification |
2020 |
16 |
Neural Pruning via Growing Regularization |
ICLR |
WF |
Greg |
- |
Image Classification |
2021 |
17 |
Trainability Preserving Nueral Structured Pruning |
ICLR |
F |
TPP |
Pytorch(Author) |
Image Classification |
2023 |
18 |
Optimal Brain Damage |
NIPS |
W |
OBD |
- |
Image Classification |
1989 |
19 |
Second Order Derivatives for Network Pruning: Optimal Brain Surgeon |
NIPS |
W |
OBS |
- |
Image Classification |
1992 |
20 |
Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Coding |
ICLR (Best) |
W |
- |
Caffe(Author) |
Image Classification |
2016 |
21 |
The State of Sparsity in Deep Neural Networks |
arXiv |
w |
- |
TensorFlow(Author) |
Image Classification&machine translation |
2019 |
22 |
Auto-Balanced Filter Pruning for Efficient Convolutional Neural Networks |
AAAI |
F |
- |
- |
Image Classification |
2019 |
23 |
Reborn filters: Pruning convolutional neural networks with limited data |
AAAI |
F |
- |
- |
Image Classification |
2020 |
24 |
|
ICLR |
F |
- |
PyTorch(Author) |
Image Classification |
2021 |
25 |
Lottery Jackpot Exist in Pre-trained Models |
arXiv |
W |
Jackpot |
[PyTorch(Author)](https://github.com/zyxxmu/lottery-jackpots) |
Image Classification |
2021 |
26 |
2PFPCE: Two-Phase Filter Pruning Based on Conditional Entropy |
AAAI |
W |
2PFPCE |
- |
Image Classification |
2018 |
27 |
Exploiting Kernel Sparsity and Entropy for Interpretable CNN Compression |
CVPR |
W |
KSE |
PyTorch(Author) |
Image Classification |
2019 |
28 |
AMC: Automl for model compression and acceleration on mobile devices |
ECCV |
F |
AMC |
TensorFlow(3rd) |
Image Classification |
2018 |
29 |
Towards Efficient Model Compression via Learned Global Ranking |
CVPR |
F |
LeGR |
Pytorch(Author) |
Image Classification |
2020 |
30 |
Collaborative Channel Pruning for Deep Networks |
ICML |
F |
CCP |
- |
Image Classification |
2019 |
31 |
ECC: Platform-Independent Energy-Constrained Deep Neural Network Compression via a Bilinear Regression Model |
CVPR |
F |
ECC |
Pytorch(Author) |
Image Classification&Semantic Segmentation |
2019 |
32 |
Discrete Model Compression With Resource Constraint for Deep Neural Networks |
CVPR |
F |
- |
- |
Image Classification |
2020 |
33 |
Network Pruning via Performance Maximization |
CVPR |
F |
NPPM |
Pytorch(Author) |
Image Classification |
2021 |
34 |
Operation-Aware Soft Channel Pruning using Differentiable Masks |
ICML |
F |
SCP |
- |
Image Classification |
2020 |
35 |
Towards Compact and Robust Deep Networks |
arXiv |
W |
- |
- |
Image Classification |
2020 |
36 |
HYDRA: Pruning Adversarially Robust Neural Networks |
NeurIPS |
W |
HYDRA |
PyTorch(Author) |
Adversarial Robustness |
2020 |
37 |
Approximated Oracle Filter Pruning for Destructive CNN Width Optimization github |
ICML |
F |
AOFP |
Pytorch(Author) |
Image Classification |
2019 |
38 |
Channel Pruning via Automatic Structure Search |
IJCAI |
F |
ABC |
PyTorch(Author) |
Image Classification |
2020 |
39 |
Group Fisher Pruning for Practical Network Compression |
ICML |
F |
GFP |
PyTorch(Author) |
Image Classification&Object Detection |
2021 |
40 |
TransTailor: Pruning the Pre-trained Model for Improved Transfer Learning |
AAAI |
F |
TransTailor |
- |
Image Classification |
2021 |
41 |
Towards Compact ConvNets via Structure-Sparsity Regularized Filter Pruning |
TNNLS |
F |
SSR |
Caffe(Author) |
Image Classification |
2019 |
42 |
Network Pruning That Matters: A Case Study on Retraining Variants |
ICLR |
F |
- |
PyTorch(Author) |
Image Classification |
2021 |
43 |
ChipNet: Budget-Aware Pruning with Heaviside Continuous Approximations |
ICLR |
F |
ChipNet |
PyTorch(Author) |
Image Classification |
2021 |
44 |
SOSP: Efficiently Capturing Global Correlations by Second-Order Structured Pruning |
ICLR (Spotlight) |
F |
SOSP |
PyTorch(Author)(Releasing) |
Image Classification |
2022 |
45 |
The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks |
ICLR (Best) |
W |
LTH |
TensorFlow(Author) |
Image Classification |
2019 |
46 |
Proving the Lottery Ticket Hypothesis for Convolutional Neural Networks |
ICML |
N |
- |
- |
- |
2020 |
47 |
Logarithmic Pruning is All You Need |
NeurIPS |
N |
- |
- |
- |
2020 |
48 |
Optimal Lottery Tickets via SUBSETSUM:Logarithmic Over-Parameterization is Sufficient |
NeurIPS |
N |
- |
PyTorch(Author) |
Image Classification |
2020 |
49 |
Sanity Checks for Lottery Tickets: Does Your Winning Ticket Really Win the Jackpot? |
NeurIPS |
W |
- |
PyTorch(Author) |
Image Classification |
2021 |
50 |
Multi-Prize Lottery Ticket Hypothesis: Finding Accurate Binary Neural Networks by Pruning A Randomly Weighted Network |
ICLR |
W |
MPTs |
PyTorch(Author) |
Image Classification |
2021 |
51 |
One ticket to win them all: generalizing lottery ticket initializations across datasets and optimizers |
NeurIPS |
W |
- |
- |
Image Classification |
2019 |
52 |
Long live the lottery: the existence of winning tickets in lifelong learning |
ICLR |
W |
- |
PyTorch(Author) |
Image Classification |
2021 |
53 |
A Unified Lottery Ticket Hypothesis for Graph Neural Networks |
ICML |
W |
- |
PyTorch(Author) |
Node Classification&Link Prediction |
2021 |
54 |
The Lottery Ticket Hypothesis for Pre-trained BERT Networks |
ICML |
W |
- |
PyTorch(Author) |
Language Modeling |
2021 |
55 |
When BERT Plays the Lottery, All Tickets Are Winning |
EMNLP |
W |
- |
PyTorch(Author) |
Language Modeling |
2020 |
56 |
Playing the Lottery with Rewards and Multiple Languages: Lottery Tickets in RL and NLP |
ICLR |
W |
- |
- |
Classic Control&Atari Game |
2020 |
57 |
Playing Lottery Tickets with Vision and Language |
AAAI |
W |
- |
- |
Vision-and-Language |
2022 |
58 |
Validating the Lottery Ticket Hypothesis with Inertial Manifold Theory |
NeurIPS |
W |
- |
- |
Image Classification |
2021 |
59 |
Deconstructing Lottery Tickets: Zeros, Signs, and the Supermask |
NeurIPS |
W |
- |
TensorFlow(Author) |
Image Classification |
2019 |
60 |
Gradient Flow in Sparse Neural Networks and How Lottery Tickets Win |
AAAI |
W |
- |
PyTorch(Author) |
Image Classification |
2022 |
61 |
Sparse Transfer Learning via Winning Lottery Tickets |
arXiv |
W |
- |
PyTorch(Author) |
Image Classification |
2019 |
62 |
How many winning tickets are there in one DNN? |
arXiv |
W |
- |
- |
Image Classification |
2020 |
63 |
CLIP-Q: Deep Network Compression Learning by In-Parallel Pruning-Quantization |
CVPR |
W |
CLIP-Q |
- |
Image Classification |
2018 |
64 |
Group Sparsity: The Hinge Between Filter Pruning and Decomposition for Network Compression |
CVPR |
F |
Hinge |
PyTorch(Author) |
Image Classification |
2020 |
65 |
Towards Compact CNNs via Collaborative Compression |
CVPR |
F |
CC |
PyTorch(Author) |
Image Classification |
2021 |
66 |
NPAS: A Compiler-aware Framework of Unified Network Pruning andArchitecture Search for Beyond Real-Time Mobile Acceleration |
CVPR |
F |
NPAS |
- |
Image Classification |
2021 |
67 |
GAN Compression: Efficient Architectures for Interactive Conditional GANs |
arXiv |
C |
- |
- |
Image-to-Image Translation |
2021 |
68 |
Content-Aware GAN Compression |
CVPR |
F |
- |
PyTorch(Author) |
Image Generation, Image Projection, Image Editing |
|
69 |
Dreaming to Prune Image Deraining Networks |
CVPR |
F |
- |
- |
Image Deraining |
2022 |
70 |
Prune Your Model Before Distill It |
ECCV |
F |
- |
PyTorch(Author) |
Image Classification |
2022 |
71 |
LadaBERT: Lightweight Adaptation of BERT through Hybrid Model Compression |
COLING |
W |
- |
- |
NLP(Sentiment Classification,Natural Language Inference,Pairwise Semantic Equivalence) |
2020 |
72 |
The Lottery Ticket Hypothesis for Object Recognition |
CVPR |
W |
- |
PyTorch(Author) |
Object Detection |
2021 |
73 |
Enabling Retrain-free Deep Neural Network Pruning Using Surrogate Lagrangian Relaxation |
IJCAI |
W |
- |
- |
Image Classification & Object Detection |
2021 |
74 |
Joint-DetNAS: Upgrade Your Detector with NAS, Pruning and Dynamic Distillation |
CVPR |
F |
Joint-DetNAS |
- |
Image Classification & Object Detection |
2021 |
75 |
Exploring Linear Relationship in Feature Map Subspace for ConvNets Compression |
arXiv |
F |
- |
- |
Object Detection&Human Pose Estimation |
2018 |
76 |
Train Large, Then Compress: Rethinking Model Size for Efficient Training and Inference of Transformers |
ICML |
W |
- |
- |
NLP |
2020 |
77 |
To prune, or not to prune: exploring the efficacy of pruning for model compression |
ICLRW |
W |
- |
TensorFlow(Author) |
NLP |
2018 |
78 |
Analyzing Multi-Head Self-Attention: Specialized Heads Do the Heavy Lifting, the Rest Can Be Pruned |
ACL |
W |
- |
PyTorch(Author) |
NLP |
2019 |
79 |
Towards Adversarial Robustness Via Compact Feature Representations |
ICASSP |
N |
- |
PyTorch(Author) |
Adversarial Robustness |
2021 |
80 |
Pruning Redundant Mappings in Transformer Models via Spectral-Normalized Identity Prior |
EMNLP |
Other |
- |
- |
NLP |
2020 |
81 |
Reweighted Proximal Pruning for Large-Scale Language Representation |
arXiv |
Other |
- |
- |
NLP |
2019 |
82 |
Efficient Transformer-based Large Scale Language Representations using Hardware-friendly Block Structured Pruning |
EMNLP |
Other |
- |
- |
NLP |
2019 |
83 |
EarlyBERT: Efficient BERT training via early-bird lottery tickets |
ACL-IJCNLP |
Other |
EarlyBERT |
PyTorch(Author) |
NLP |
2021 |
84 |
Movement Pruning: Adaptive Sparsity by Fine-Tuning |
NeurIPS |
W |
- |
PyTorch(Author) |
NLP |
2020 |
85 |
Audio Lottery: Speech Recognition made ultra-lightweight, transferable, and noise-robust |
ICLR |
W |
- |
PyTorch(Author) |
Speach Recognition |
2022 |
86 |
PARP: Prune, Adjust and Re-Prune for Self-Supervised Speech Recognition |
NeurIPS |
W |
PARP |
- |
Speach Recognition |
2021 |
87 |
Dynamic Sparsity Neural Networks for Automatic Speech Recognition |
ICASSP |
W |
- |
- |
Speach Recognition |
2021 |
88 |
On the Predictability of Pruning Across Scales |
ICML |
W |
- |
- |
Image Classification |
2021 |
89 |
How much pre-training is enough to discover a good subnetwork? |
arXiv |
W |
- |
- |
Image Classification |
2021 |
90 |
On the Transferability of Winning Tickets in Non-Natural Image Datasets |
arXiv |
W |
- |
- |
Image Classification |
2020 |
91 |
The Lottery Tickets Hypothesis for Supervised and Self-supervised Pre-training in Computer Vision Models |
CVPR |
W |
- |
PyTorch(Author) |
Image Classification |
2021 |
92 |
DepGraph: Towards Any Structural Pruning |
CVPR |
DepGraph |
- |
PyTorch(Author) |
CV/NLP |
2023 |
93 |
How Well Do Sparse ImageNet Models Transfer? |
CVPR |
W |
- |
PyTorch(Author) |
Image Classification&Object Detection |
2022 |
94 |
The Elastic Lottery Ticket Hypothesis |
NeurIPS |
W |
E-LTH |
PyTorch(Author) |
Image Classification |
2021 |
95 |
Why Lottery Ticket Wins? A Theoretical Perspective of Sample Complexity on Sparse Neural Networks |
NeurIPS |
W |
- |
- |
Image Classification |
2021 |
96 |
Exploring Lottery Ticket Hypothesis in Spiking Neural Networks |
ECCV |
W |
ET |
PyTorch(Author) |
Image Classification |
2022 |
97 |
Graph Pruning for Model Compression |
Applied Intelligence |
W |
GraphPruning |
- |
Image Classification |
2022 |
98 |
Accelerating Convolutional Networks via Global & Dynamic Filter Pruning |
IJCAI |
F |
GDP |
- |
Image Classification |
2018 |
99 |
DMCP: Differentiable Markov Channel Pruning for Neural Networks |
CVPR |
C |
DMCP |
- |
Image Classification |
2020 |
100 |
Advancing Model Pruning via Bi-level Optimization |
NeurIPS |
WC |
BiP |
PyTorch(Author) |
Image Classification |
2022 |
101 |
Structured Pruning Learns Compact and Accurate Models |
ACL |
LH |
CoFi |
PyTorch(Author) |
Natural Language Understanding |
2022 |
102 |
The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for Large Language Models |
EMNLP |
W |
oBERT |
PyTorch(Author) |
Natural Language Understanding |
2022 |
103 |
Lottery Jackpots Exist in Pre-trained Models |
arXiv |
W |
oBERT |
PyTorch(Author) |
Image Classification |
2022 |