Type | F |
W |
Other |
---|---|---|---|
Explanation | Filter pruning | Weight pruning | other types |
Conf |
2015 |
2016 |
2017 |
2018 |
2019 |
2020 |
2021 |
---|---|---|---|---|---|---|---|
AAAI |
539 |
548 |
649 |
938 |
1147 |
1591 |
1692 |
ICLR |
oral-15 |
198 |
336(23) |
502(24) |
687 |
860(53) |
|
CVPR |
602(71) |
643(83) |
783(71) |
979(70) |
1300(288) |
1470(335) |
Feb.28(7015) |
NeurIPS |
479 |
645 |
954 |
1011 |
1428 |
1900 (105) |
|
ICML |
433 |
621 |
774 |
1088 |
May.8th | ||
IJCAI |
572 |
551 |
660 |
710 |
850 |
592 |
|
ICCV |
- |
621 |
- |
1077 | - |
||
ECCV |
415 |
- |
778 |
- |
1360 |
||
MLsys |
|||||||
ISCA |
57 | 54 | 54 | 63 | 62 | 77 | |
ECAI |
- |
562 | - |
656 | - |
365 |
MLsys
:https://proceedings.mlsys.org/paper/2019
ICCV
https://dblp.org/db/conf/iccv/iccv2019.html
ICCV
https://dblp.org/db/conf/iccv/iccv2017.html
ECCV
https://link.springer.com/conference/eccv
ECCV
https://zhuanlan.zhihu.com/p/157569669
CVPR
https://dblp.org/db/conf/cvpr/cvpr2020.html
ICDE
ACCV
WACV
BMVC
WACV
:(Applications of Computer Vision)
nsdi
sigcomm
osdi
sosp
sigmod
mobicom
sosp
ATC
MLsys
- Diversifying Sample Generation for Accurate Data-Free Quantization
- BRECQ: Pushing the Limit of Post-Training Quantization by Block Reconstruction
- Learnable Companding Quantization for Accurate Low-bit Neural Networks
- UMEC: Unified Model and Embedding Compression for Efficient Recommendation Systems
- Distribution Adaptive INT8 Quantization for Training CNNs
- Refine Myself by Teaching Myself: Feature Refinement via Self-Knowledge Distillation
- You Only Look One-level Feature
- Probabilistic two-stage detection
- General Instance Distillation for Object Detection
- Generalized Focal Loss V2: Learning Reliable Localization Quality Estimation for Dense Object Detection
- Sparse R-CNN: End-to-End Object Detection with Learnable Proposals
- Relation Networks for Object Detection
- RelationNet++: Bridging Visual Representations for Object Detection via Transformer Decoder
- Weighted boxes fusion: Ensembling boxes from different object detection models
- Dert:End-to-End Object Detection with Transformers
- Fine-grained Angular Contrastive Learning with Coarse Labels(😮oral) 使用自监督进行 Coarse Labels(粗标签)的细粒度分类方面的工作。粗标签与细粒度标签相比,更容易和更便宜,因为细粒度标签通常需要域专家。
- UP-DETR: Unsupervised Pre-training for Object Detection with Transformers(oral)
- ReXNet: Diminishing Representational Bottleneck on Convolutional Neural Network
- RepVGG: Making VGG-style ConvNets Great Again
- Revisiting Dynamic Convolution via Matrix Decomposition
- An Energy and GPU-Computation Efficient Backbone Network for Real-Time Object Detection
- CenterMask : Real-Time Anchor-Free Instance Segmentation
- VarGNet: Variable Group Convolutional Neural Network for Efficient Embedded Computing
- Manifold Regularized Dynamic Network Pruning
- Fast and Accurate Model Scaling
- Pyramid Vision Transformer: A Versatile Backbone for Dense Prediction without Convolutions
- Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecasting
- On Implicit Filter Level Sparsity in Convolutional Neural Networks 29.Skip-Convolutions for Efficient Video Processing CVPR2021 30.FIXUP INITIALIZATION: RESIDUAL LEARNING WITHOUT NORMALIZATION ICLR 2019 31.ReZero is All You Need: Fast Convergence at Large Depth 2020.May 32.Going deeper with Image Transformers 2021
- Batch Normalization Biases Residual Blocks Towards the Identity Function in Deep Networks NeurIPS 2020 34.Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecasting AAAI2021 35.WaveNet
2019 SeerNet: Predicting Convolutional Neural Network Feature-Map Sparsity Through Low-Bit Quantization CVPR
- Any-Precision Deep Neural Networks
AAAI
- Post-training Quantization with Multiple Points Mixed Precision without Mixed Precision |
AAAI
Mixed Precision
- CPT:Efficient deep neural network training via cyclic precision
ICLR
- Precision Gating Improving Neural Network Efficiency with Dynamic Dual-Precision Activations
ICLR
- Post-training Quantization with Multiple Points Mixed Precision without Mixed Precision
ICML
- Towards Unified INT8 Training for Convolutional Neural Network |
CVPR
商汤bp+qat
- APoT-addive powers-of-two quantization an efficient non-uniform discretization for neural networks
ICLR
非线性量化scheme
- Post-Training Piecewise Linear Quantization for Deep Neural Networks
ECCV
(oral) - Training Quantized Neural Networks With a Full-Precision Auxiliary Module
CVPR
(oral) - MCUNet: Tiny Deep Learning on IoT Devices
NeurIPS
- HAWQ-V2 Hessian Aware trace-Weighted Quantization of Neural Networks
NeurIPS
- HAWQ-V3: Dyadic Neural Network Quantization
- Subtensor Quantization for Mobilenets
-
Mobilenets
- Generative Low-bitwidth Data Free Quantization
ECCV
GAN
- EagleEye: Fast Sub-net Evaluation for Efficient Neural Network Pruning
ECCV
(Oral)F
PyTorch(Author) DSA: More Efficient Budgeted Pruning via Differentiable Sparsity AllocationECCV
F
- AutoCompress: An Automatic DNN Structured Pruning Framework for Ultra-High Compression Rates
AAAI
F
- - Pruning from Scratch
AAAI
Other
- - DHP: Differentiable Meta Pruning via HyperNetworks
ECCV
F
PyTorch(Author) - Towards Efficient Model Compression via Learned Global Ranking
CVPR
(Oral)F
Pytorch(Author) - HRank: Filter Pruning using High-Rank Feature Map
CVPR
(Oral)F
可 - 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
Mask
-
ACIQ-Analytical Clipping for Integer Quantization of Neural Networks
ICLR
-
Differentiable Quantization of Deep Neural Networks
NeurIPS
没代码+NAS
-
Post training 4-bit quantization of convolutional networks for rapid-deployment
NeurIPS
ACIQ -
Data-Free Quantization Through Weight Equalization and Bias Correction
ICCV
(Oral) -
Data-Free Quantization Through Weight Equalization and Bias Correction
ICCV
-
HAWQ: Hessian AWare Quantization of Neural Networks with Mixed-Precision
ICCV
(Poster)可微分
-
**(DSQ)**Differentiable Soft Quantization: Bridging Full-Precision and Low-Bit Neural Networks
ICCV
可微分
-
Low-bit Quantization of Neural Networks for Efficient Inference
ICCV Workshops
没代码
-
Quantization Networks
CVPR
可微分 -
Fully Quantized Network for Object Detection
CVPR
没代码 -
HAQ Hardware-Aware Automated Quantization With Mixed Precision
CVPR
RL
-
Accelerating Convolutional Neural Networks via Activation Map Compression
CVPR
没代码
-
Learning to quantize deep networks by optimizing quantization intervals with task loss
CVPR
可微分
-
Accelerating Convolutional Neural Networks via Activation Map Compression
CVPR
没看懂pipeline
-
Fighting Quantization Bias With Bias
CVPR W
给量化误差补偿bias -
Learning low-precision neural networks without Straight-Through Estimator(STE)
IJCAI
可微分
-
OCS-Improving Neural Network Quantization without Retraining using Outlier Channel Splitting. |
ICML
-
Same, Same But Different Recovering Neural Network Quantization Error Through Weight Factorization
ICML
与高通的DFQ很像
-
Learning low-precision neural networks without Straight-Through Estimator (STE)
IJCAI
没代码+可微分
-
SeerNet Predicting Convolutional Neural Network Feature-Map Sparsity Through Low-Bit Quantization
ECCV
稀疏化
-
DAC Data-free Automatic Acceleration of Convolutional Networks
WACV
DW Conv
-
A Quantization-Friendly Separable Convolution for MobileNets
-
MobileNets
-
The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks
ICLR
(Best)W
winning ticket
-
Rethinking the Value of Network Pruning
ICLR
F
slim prune -
Filter Pruning via Geometric Median for Deep Convolutional Neural Networks Acceleration
CVPR
(Oral)F
基于几何平均数
-
Importance Estimation for Neural Network Pruning
CVPR
F
Nvidia
-
Centripetal SGD for Pruning Very Deep Convolutional Networks with Complicated Structure
CVPR
F
聚类
- PACT: Parameterized Clipping Activation for Quantized Neural Networks |
ICLR
- Scalable methods for 8-bit training of neural networks |
NeurIPS
- Two-step quantization for low-bit neural networks |
CVPR
- Quantization and Training of Neural Networks for Efficient Integer-Arithmetic-Only Inference |
CVPR
**QAT和fold Bn**
- Joint training of low-precision neural network with quantization interval Parameters |
NeurIPS
- Lq-nets Learned quantization for highly accurate and compact deep neural networks |
ECCV
- Apprentice Using KD Techniques to Improve Low-Precision Network Accuracy |
ICLR
- calable Methods for 8-bit Training of Neura Network |
NeurIPS
| | - Quantization mimic Towards very tiny cnn for object detection |
ECCV
| | KD+量化 - Mimicking very efficient network for object detection |
CVPR
| | 跟上面 - Training and inference with integers in deep neural networks |
ICLR
| |WAGE
- Rethinking the Smaller-Norm-Less-Informative Assumption in Channel Pruning of Convolution Layers |
ICLR
|F
| ISAT+质疑了norm-based - A Systematic DNN Weight Pruning Framework using Alternating Direction Method of Multipliers |
ECCV
|w
| ADMM - Amc: Automl for model compression and acceleration on mobile devices |
ECCV
|F
| 还没看 - Soft Filter Pruning for Accelerating Deep Convolutional Neural Networks |
IJCAI
|F
| 剪枝后还可以恢复 - Data-Driven Sparse Structure Selection for Deep Neural Networks |
ECCV
|F
| APG +Bn
- Pruning Filters for Efficient ConvNets |
ICLR
|F
| abs(filter) - Pruning Convolutional Neural Networks for Resource Efficient Inference |
ICLR
|F
| 基于一阶泰勒展开近似 - ThiNet: A Filter Level Pruning Method for Deep Neural Network Compression |
ICCV
|F
| 找一组channel近似全集 - Channel pruning for accelerating very deep neural networks |
ICCV
|F
| LASSO回归、孙剑 - Learning Efficient Convolutional Networks Through Network Slimming |
ICCV
|F
| 基于BN层 - Runtime Neural Pruning |
NeurIPS
| | Markov+RL - Network trimming A data-driven neuron pruning approach towards efficient deep architectures |
NeurIPS
| | APoZ
- HWGQ-Deep Learning With Low Precision by Half-wave Gaussian Quantization |
CVPR
| | 孙剑 - Weighted-Entropy-based Quantization for Deep Neural Networks |
CVPR
| |not code
| - WRPN Wide Reduced-Precision Networks |
ICLR
| |intel
+distiller框架集成 | - DoReFa-Net: training low bitwidth convolutional neural networks with low bitwidth gradients |
ICLR
| | 超低bit - XNOR-Net: ImageNet Classification Using Binary Convolutional Neural Networks |
ECCV
| | 超低bit - Binaryconnect Training deep neural networks with binary weights during propagations |
NeurIPS
| | 超低bit - INQ-Incremental network quantization Towards lossless cnns with low-precision weight |
ICLR
| |intel
- Convolutional Neural Networks using Logarithmic Data Representation |
ICML
| | scheme