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Post-training Model Quantization Using GANs for Synthetic Data
  Generation

Post-training Model Quantization Using GANs for Synthetic Data Generation

10 May 2023
Athanasios Masouris
Mansi Sharma
Adrian Boguszewski
Alexander Kozlov
Zhuo Wu
Raymond Lo
    MQ
ArXiv (abs)PDFHTMLGithub (9★)

Papers citing "Post-training Model Quantization Using GANs for Synthetic Data Generation"

32 / 32 papers shown
Title
Generative Adversarial Networks
Generative Adversarial Networks
Gilad Cohen
Raja Giryes
GAN
298
30,149
0
01 Mar 2022
Post-training deep neural network pruning via layer-wise calibration
Post-training deep neural network pruning via layer-wise calibration
Ivan Lazarevich
Alexander Kozlov
Nikita Malinin
3DPC
62
27
0
30 Apr 2021
RepVGG: Making VGG-style ConvNets Great Again
RepVGG: Making VGG-style ConvNets Great Again
Xiaohan Ding
Xinming Zhang
Ningning Ma
Jungong Han
Guiguang Ding
Jian Sun
286
1,602
0
11 Jan 2021
TinyGAN: Distilling BigGAN for Conditional Image Generation
TinyGAN: Distilling BigGAN for Conditional Image Generation
Ting-Yun Chang
Chi-Jen Lu
GAN
43
27
0
29 Sep 2020
Training Generative Adversarial Networks with Limited Data
Training Generative Adversarial Networks with Limited Data
Tero Karras
M. Aittala
Janne Hellsten
S. Laine
J. Lehtinen
Timo Aila
GAN
161
1,887
0
11 Jun 2020
Up or Down? Adaptive Rounding for Post-Training Quantization
Up or Down? Adaptive Rounding for Post-Training Quantization
Markus Nagel
Rana Ali Amjad
M. V. Baalen
Christos Louizos
Tijmen Blankevoort
MQ
90
586
0
22 Apr 2020
ZeroQ: A Novel Zero Shot Quantization Framework
ZeroQ: A Novel Zero Shot Quantization Framework
Yaohui Cai
Z. Yao
Zhen Dong
A. Gholami
Michael W. Mahoney
Kurt Keutzer
MQ
98
399
0
01 Jan 2020
Analyzing and Improving the Image Quality of StyleGAN
Analyzing and Improving the Image Quality of StyleGAN
Tero Karras
S. Laine
M. Aittala
Janne Hellsten
J. Lehtinen
Timo Aila
GAN
314
5,828
0
03 Dec 2019
Data-Free Quantization Through Weight Equalization and Bias Correction
Data-Free Quantization Through Weight Equalization and Bias Correction
Markus Nagel
M. V. Baalen
Tijmen Blankevoort
Max Welling
MQ
75
515
0
11 Jun 2019
Fighting Quantization Bias With Bias
Fighting Quantization Bias With Bias
Alexander Finkelstein
Uri Almog
Mark Grobman
MQ
77
57
0
07 Jun 2019
Low-bit Quantization of Neural Networks for Efficient Inference
Low-bit Quantization of Neural Networks for Efficient Inference
Yoni Choukroun
Eli Kravchik
Fan Yang
P. Kisilev
MQ
72
364
0
18 Feb 2019
Improving Neural Network Quantization without Retraining using Outlier
  Channel Splitting
Improving Neural Network Quantization without Retraining using Outlier Channel Splitting
Ritchie Zhao
Yuwei Hu
Jordan Dotzel
Christopher De Sa
Zhiru Zhang
OODDMQ
101
311
0
28 Jan 2019
ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture
  Design
ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design
Ningning Ma
Xiangyu Zhang
Haitao Zheng
Jian Sun
181
5,006
0
30 Jul 2018
LQ-Nets: Learned Quantization for Highly Accurate and Compact Deep
  Neural Networks
LQ-Nets: Learned Quantization for Highly Accurate and Compact Deep Neural Networks
Dongqing Zhang
Jiaolong Yang
Dongqiangzi Ye
G. Hua
MQ
63
703
0
26 Jul 2018
Scalable Methods for 8-bit Training of Neural Networks
Scalable Methods for 8-bit Training of Neural Networks
Ron Banner
Itay Hubara
Elad Hoffer
Daniel Soudry
MQ
84
339
0
25 May 2018
MobileNetV2: Inverted Residuals and Linear Bottlenecks
MobileNetV2: Inverted Residuals and Linear Bottlenecks
Mark Sandler
Andrew G. Howard
Menglong Zhu
A. Zhmoginov
Liang-Chieh Chen
204
19,333
0
13 Jan 2018
Channel Pruning for Accelerating Very Deep Neural Networks
Channel Pruning for Accelerating Very Deep Neural Networks
Yihui He
Xiangyu Zhang
Jian Sun
204
2,529
0
19 Jul 2017
Pruning Filters for Efficient ConvNets
Pruning Filters for Efficient ConvNets
Hao Li
Asim Kadav
Igor Durdanovic
H. Samet
H. Graf
3DPC
195
3,705
0
31 Aug 2016
Improved Techniques for Training GANs
Improved Techniques for Training GANs
Tim Salimans
Ian Goodfellow
Wojciech Zaremba
Vicki Cheung
Alec Radford
Xi Chen
GAN
486
9,067
0
10 Jun 2016
Deep Residual Learning for Image Recognition
Deep Residual Learning for Image Recognition
Kaiming He
Xinming Zhang
Shaoqing Ren
Jian Sun
MedIm
2.2K
194,426
0
10 Dec 2015
Rethinking the Inception Architecture for Computer Vision
Rethinking the Inception Architecture for Computer Vision
Christian Szegedy
Vincent Vanhoucke
Sergey Ioffe
Jonathon Shlens
Z. Wojna
3DVBDL
886
27,416
0
02 Dec 2015
Fixed Point Quantization of Deep Convolutional Networks
Fixed Point Quantization of Deep Convolutional Networks
D. Lin
S. Talathi
V. Annapureddy
MQ
104
816
0
19 Nov 2015
BinaryConnect: Training Deep Neural Networks with binary weights during
  propagations
BinaryConnect: Training Deep Neural Networks with binary weights during propagations
Matthieu Courbariaux
Yoshua Bengio
J. David
MQ
212
2,992
0
02 Nov 2015
Deep Compression: Compressing Deep Neural Networks with Pruning, Trained
  Quantization and Huffman Coding
Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Coding
Song Han
Huizi Mao
W. Dally
3DGS
263
8,859
0
01 Oct 2015
Data-free parameter pruning for Deep Neural Networks
Data-free parameter pruning for Deep Neural Networks
Suraj Srinivas
R. Venkatesh Babu
3DPC
82
547
0
22 Jul 2015
Learning both Weights and Connections for Efficient Neural Networks
Learning both Weights and Connections for Efficient Neural Networks
Song Han
Jeff Pool
J. Tran
W. Dally
CVBM
313
6,700
0
08 Jun 2015
Compressing Neural Networks with the Hashing Trick
Compressing Neural Networks with the Hashing Trick
Wenlin Chen
James T. Wilson
Stephen Tyree
Kilian Q. Weinberger
Yixin Chen
165
1,191
0
19 Apr 2015
Distilling the Knowledge in a Neural Network
Distilling the Knowledge in a Neural Network
Geoffrey E. Hinton
Oriol Vinyals
J. Dean
FedML
364
19,733
0
09 Mar 2015
Deep Learning with Limited Numerical Precision
Deep Learning with Limited Numerical Precision
Suyog Gupta
A. Agrawal
K. Gopalakrishnan
P. Narayanan
HAI
207
2,049
0
09 Feb 2015
Going Deeper with Convolutions
Going Deeper with Convolutions
Christian Szegedy
Wei Liu
Yangqing Jia
P. Sermanet
Scott E. Reed
Dragomir Anguelov
D. Erhan
Vincent Vanhoucke
Andrew Rabinovich
485
43,694
0
17 Sep 2014
Very Deep Convolutional Networks for Large-Scale Image Recognition
Very Deep Convolutional Networks for Large-Scale Image Recognition
Karen Simonyan
Andrew Zisserman
FAttMDE
1.7K
100,508
0
04 Sep 2014
Do Deep Nets Really Need to be Deep?
Do Deep Nets Really Need to be Deep?
Lei Jimmy Ba
R. Caruana
169
2,119
0
21 Dec 2013
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