Built on

Optimal weighted nearest neighbour classifiers
R. J. Samworth · 2013
Earlier work this paper cites.
Very deep convolutional networks for large-scale image recognition
Original
K. Simonyan and A. Zisserman · 2014
Earlier work this paper cites.
Going deeper with convolutions
Original
C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. E. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, and A. Rabinovich · 2014
Earlier work this paper cites.
Binaryconnect: Training deep neural networks with binary weights during propagations
M. Courbariaux, Y. Bengio, and J.-P. David · 2015
Earlier work this paper cites.
Training deep neural networks with low precision multiplications
Original
M. Courbariaux, J.-P. David, and Y. Bengio · 2015
Earlier work this paper cites.
The pascal visual object classes challenge: A retrospective
M. Everingham, S. M. A. Eslami, L. Van Gool, C. K. I. Williams, J. Winn, and A. Zisserman · 2015
Earlier work this paper cites.
Deep compression: Compressing deep neural networks with pruning, trained quantization and huffman coding
Original
S. Han, H. Mao, and W. J. Dally · 2015
Earlier work this paper cites.
Deep residual learning for image recognition
Original
K. He, X. Zhang, S. Ren, and J. Sun · 2015
Earlier work this paper cites.
Batch normalization: Accelerating deep network training by reducing internal covariate shift
Original
S. Ioffe and C. Szegedy · 2015
Earlier work this paper cites.
Rethinking the inception architecture for computer vision
Original
C. Szegedy, V. Vanhoucke, S. Ioffe, J. Shlens, and Z. Wojna · 2015
Earlier work this paper cites.

Similar

Xception: Deep learning with depthwise separable convolutions
Original
F. Chollet · 2016
Cited alongside, same era.
Ristretto: Hardware-oriented approximation of convolutional neural networks
Original
P. Gysel · 2016
Cited alongside, same era.
Quantized neural networks: Training neural networks with low precision weights and activations
I. Hubara, M. Courbariaux, D. Soudry, R. El-Yaniv, and Y. Bengio · 2016
Cited alongside, same era.
Squeezenet: Alexnet-level accuracy with 50x fewer parameters and <0.5mb model size
Original
F. N. Iandola, M. W. Moskewicz, K. Ashraf, S. Han, W. J. Dally, and K. Keutzer · 2016
Cited alongside, same era.
Ternary weight networks
Original
F. Li, B. Zhang, and B. Liu · 2016

Then

Dorefa-net: Training low bitwidth convolutional neural networks with low bitwidth gradients
Original
S. Zhou, Y. Wu, Z. Ni, X. Zhou, H. Wen, and Y. Zou · 2016
Later among the works it cites.
Towards the limit of network quantization
Original
Y. Choi, M. El-Khamy, and J. Lee · 2017
Later among the works it cites.
Mobilenets: Efficient convolutional neural networks for mobile vision applications
Original
A. G. Howard, M. Zhu, B. Chen, D. Kalenichenko, W. Wang, T. Weyand, M. Andreetto, and H. Adam · 2017
Later among the works it cites.
Extremely low bit neural network: Squeeze the last bit out with admm
Original
C. Leng, H. Li, S. Zhu, and R. Jin · 2017
Later among the works it cites.
Towards accurate binary convolutional neural network

Beyond the bibliography

alphaXiv searches the wider corpus for related work and actual follow-ups.

Open on alphaXiv

alphaXiv is searching for related work…

Reading the bibliography…

2018

Quantization for Rapid Deployment of Deep Neural Networks

Lee, Jun Haeng, Ha, Sangwon, Choi, Saerom et al.

Understand

This paper aims at rapid deployment of the state-of-the-art deep neural networks (DNNs) to energy efficient accelerators without time-consuming fine tuning or the availability of the full datasets.

Converting DNNs in full precision to limited precision is essential in taking advantage of the accelerators with reduced memory footprint and computation power.
However, such a task is not trivial since it often requires the full training and validation datasets for profiling the network statistics and fine tuning the networks to recover the accuracy lost after quantization.
To address these issues, we propose a simple method recognizing channel-level distribution to reduce the quantization-induced accuracy loss and minimize the required image samples for profiling.

Built on

Optimal weighted nearest neighbour classifiers
R. J. Samworth · 2013
Earlier work this paper cites.
Very deep convolutional networks for large-scale image recognition
Original
K. Simonyan and A. Zisserman · 2014
Earlier work this paper cites.
Going deeper with convolutions
Original
C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. E. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, and A. Rabinovich · 2014
Earlier work this paper cites.
Binaryconnect: Training deep neural networks with binary weights during propagations
M. Courbariaux, Y. Bengio, and J.-P. David · 2015
Earlier work this paper cites.
Training deep neural networks with low precision multiplications
Original
M. Courbariaux, J.-P. David, and Y. Bengio · 2015
Earlier work this paper cites.
The pascal visual object classes challenge: A retrospective
M. Everingham, S. M. A. Eslami, L. Van Gool, C. K. I. Williams, J. Winn, and A. Zisserman · 2015
Earlier work this paper cites.
Deep compression: Compressing deep neural networks with pruning, trained quantization and huffman coding
Original
S. Han, H. Mao, and W. J. Dally · 2015
Earlier work this paper cites.
Deep residual learning for image recognition
Original
K. He, X. Zhang, S. Ren, and J. Sun · 2015
Earlier work this paper cites.
Batch normalization: Accelerating deep network training by reducing internal covariate shift
Original
S. Ioffe and C. Szegedy · 2015
Earlier work this paper cites.
Rethinking the inception architecture for computer vision
Original
C. Szegedy, V. Vanhoucke, S. Ioffe, J. Shlens, and Z. Wojna · 2015
Earlier work this paper cites.

Similar

Xception: Deep learning with depthwise separable convolutions
Original
F. Chollet · 2016
Cited alongside, same era.
Ristretto: Hardware-oriented approximation of convolutional neural networks
Original
P. Gysel · 2016
Cited alongside, same era.
Quantized neural networks: Training neural networks with low precision weights and activations
I. Hubara, M. Courbariaux, D. Soudry, R. El-Yaniv, and Y. Bengio · 2016
Cited alongside, same era.
Squeezenet: Alexnet-level accuracy with 50x fewer parameters and <0.5mb model size
Original
F. N. Iandola, M. W. Moskewicz, K. Ashraf, S. Han, W. J. Dally, and K. Keutzer · 2016
Cited alongside, same era.
Ternary weight networks
Original
F. Li, B. Zhang, and B. Liu · 2016