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Learning convolutional neural networks (CNNs) with low bitwidth is challenging because performance may drop significantly after quantization.
Learning multiple layers of features from tiny images
A. Krizhevsky, G. Hinton, et al · 2009
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Estimating or propagating gradients through stochastic neurons for conditional computation
Y. Bengio, N. Léonard, and A. Courville · 2013
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Fast training of convolutional networks through ffts
M. Mathieu, M. Henaff, and Y. LeCun · 2013
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Spectral representations for convolutional neural networks
O. Rippel, J. Snoek, and R. P. Adams · 2015
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Spectral representations for convolutional neural networks
O. Rippel, J. Snoek, and R. P. Adams · 2015
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Imagenet large scale visual recognition challenge
O. Russakovsky, J. Deng, H. Su, J. Krause, S. Satheesh, S. Ma, Z. Huang, A. Karpathy, A. Khosla, M. Bernstein, et al · 2015
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Deep residual learning for image recognition
K. He, X. Zhang, S. Ren, and J. Sun · 2016
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Energy efficient techniques using fft for deep convolutional neural networks
N. Nguyen-Thanh, H. Le-Duc, D.-T. Ta, and V.-T. Nguyen · 2016
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Xnor-net: Imagenet classification using binary convolutional neural networks
M. Rastegari, V. Ordonez, J. Redmon, and A. Farhadi · 2016
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Cnnpack: Packing convolutional neural networks in the frequency domain
Y. Wang, C. Xu, S. You, D. Tao, and C. Xu · 2016
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Dorefa-net: Training low bitwidth convolutional neural networks with low bitwidth gradients
S. Zhou, Y. Wu, Z. Ni, X. Zhou, H. Wen, and Y. Zou · 2016
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Wrpn: wide reduced-precision networks
A. Mishra, E. Nurvitadhi, J. J. Cook, and D. Marr · 2017
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Nice: Noise injection and clamping estimation for neural network quantization
C. Baskin, N. Liss, Y. Chai, E. Zheltonozhskii, E. Schwartz, R. Giryes, A. Mendelson, and A. M. Bronstein · 2018
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Uniq: Uniform noise injection for non-uniform quantization of neural networks
C. Baskin, E. Schwartz, E. Zheltonozhskii, N. Liss, R. Giryes, A. M. Bronstein, and A. Mendelson · 2018
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Pact: Parameterized clipping activation for quantized neural networks
J. Choi, Z. Wang, S. Venkataramani, P. I.-J. Chuang, V. Srinivasan, and K. Gopalakrishnan · 2018
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Syq: Learning symmetric quantization for efficient deep neural networks
J. Faraone, N. Fraser, M. Blott, and P. H. Leong · 2018
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Quantizing deep convolutional networks for efficient inference: A whitepaper
R. Krishnamoorthi · 2018
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Relaxed quantization for discretized neural networks
C. Louizos, M. Reisser, T. Blankevoort, E. Gavves, and M. Welling · 2018
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Mobilenetv2: Inverted residuals and linear bottlenecks
M. Sandler, A. Howard, M. Zhu, A. Zhmoginov, and L.-C. Chen · 2018
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Distill-and-compare: Auditing black-box models using transparent model distillation
S. Tan, R. Caruana, G. Hooker, and Y. Lou · 2018
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Patient knowledge distillation for bert model compression
S. Sun, Y. Cheng, Z. Gan, and J. Liu · 2019
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Haq: Hardware-aware automated quantization with mixed precision
K. Wang, Z. Liu, Y. Lin, J. Lin, and S. Han · 2019
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Online learned continual compression with adaptive quantization modules
L. Caccia, E. Belilovsky, M. Caccia, and J. Pineau · 2020
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Acceleration of convolutional neural network using fft-based split convolutions
K. Chitsaz, M. Hajabdollahi, N. Karimi, S. Samavi, and S. Shirani · 2020
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Learned step size quantization
S. K. Esser, J. L. McKinstry, D. Bablani, R. Appuswamy, and D. S. Modha · 2020
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Deep neural network compression by in-parallel pruning-quantization
F. Tung and G. Mori · 2018
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Two-step quantization for low-bit neural networks
P. Wang, Q. Hu, Y. Zhang, C. Zhang, Y. Liu, and J. Cheng · 2018
Cited alongside, same era.
Training and inference with integers in deep neural networks
S. Wu, G. Li, F. Chen, and L. Shi · 2018
Cited alongside, same era.
Lq-nets: Learned quantization for highly accurate and compact deep neural networks
D. Zhang, J. Yang, D. Ye, and G. Hua · 2018
Cited alongside, same era.
Post training 4-bit quantization of convolutional networks for rapid-deployment
R. Banner, Y. Nahshan, and D. Soudry · 2019
Cited alongside, same era.
Differentiable soft quantization: Bridging full-precision and low-bit neural networks
R. Gong, X. Liu, S. Jiang, T. Li, P. Hu, J. Lin, F. Yu, and J. Yan · 2019
Cited alongside, same era.
Learning to quantize deep networks by optimizing quantization intervals with task loss
S. Jung, C. Son, S. Lee, J. Son, J.-J. Han, Y. Kwak, S. J. Hwang, and C. Choi · 2019
Cited alongside, same era.
H. V. Habi, R. H. Jennings, and A. Netzer · 2020
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Operation-aware soft channel pruning using differentiable masks
M. Kang and B. Han · 2020
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Additive powers-of-two quantization: A non-uniform discretization for neural networks
Y. Li, X. Dong, and W. Wang · 2020
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Hrank: Filter pruning using high-rank feature map
M. Lin, R. Ji, Y. Wang, Y. Zhang, B. Zhang, Y. Tian, and L. Shao · 2020
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Up or down? adaptive rounding for post-training quantization
M. Nagel, R. A. Amjad, M. van Baalen, C. Louizos, and T. Blankevoort · 2020
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Forward and backward information retention for accurate binary neural networks
H. Qin, R. Gong, X. Liu, M. Shen, Z. Wei, F. Yu, and J. Song · 2020
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Differentiable joint pruning and quantization for hardware efficiency
Y. Wang, Y. Lu, and T. Blankevoort · 2020
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M. Xiao, S. Zheng, C. Liu, Y. Wang, D. He, G. Ke, J. Bian, Z. Lin, and T.-Y. Liu · 2020
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Learning in the frequency domain
K. Xu, M. Qin, F. Sun, Y. Wang, Y.-K. Chen, and F. Ren · 2020
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