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To bridge the ever increasing gap between deep neural networks' complexity and hardware capability, network quantization has attracted more and more research attention.
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Very Deep Convolutional Networks for Large-Scale Image Recognition
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Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
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Going Deeper with Convolutions
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Deep Residual Learning for Image Recognition
He, K.; Zhang, X.; Ren, S.; and Sun, J. 2016 · 2016
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Xnor-net: Imagenet Classification Using Binary Convolutional Neural Networks
Rastegari, M.; Ordonez, V.; Redmon, J.; and Farhadi, A. 2016 · 2016
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Dorefa-net: Training low bitwidth convolutional neural networks with low bitwidth gradients
Zhou, S.; Wu, Y.; Ni, Z.; Zhou, X.; Wen, H.; and Zou, Y. 2016 · 2016
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Deep Learning with Low Precision by Half-Wave Gaussian Quantization
Cai, Z.; He, X.; Sun, J.; and Vasconcelos, N. 2017 · 2017
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Mobilenets: Efficient Convolutional Neural Networks for Mobile Vision Applications
Howard, A. G.; Zhu, M.; Chen, B.; Kalenichenko, D.; Wang, W.; Weyand, T.; Andreetto, M.; and Adam, H. 2017 · 2017
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BinaryDuo: Reducing Gradient Mismatch in Binary Activation Network by Coupling Binary Activations
Kim, H.; Kim, K.; Kim, J.; and Kim, J.-J. 2019 · 2019
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Similarity of Neural Network Representations Revisited
Kornblith, S.; Norouzi, M.; Lee, H.; and Hinton, G. 2019 · 2019
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Circulant binary convolutional networks: Enhancing the performance of 1-bit dcnns with circulant back propagation
Liu, C.; Ding, W.; Xia, X.; Zhang, B.; Gu, J.; Liu, J.; Ji, R.; and Doermann, D. 2019 · 2019
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Data-Free Quantization through Weight Equalization and Bias Correction
Nagel, M.; Baalen, M. v.; Blankevoort, T.; and Welling, M. 2019 · 2019
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Haq: Hardware-Aware Automated Quantization with Mixed Precision
Wang, K.; Liu, Z.; Lin, Y.; Lin, J.; and Han, S. 2019 · 2019
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Incremental Network Quantization: Towards Lossless CNNs with Low-Precision Weights
Zhou, A.; Yao, A.; Guo, Y.; Xu, L.; and Chen, Y. 2017 · 2017
Cited alongside, same era.
Pact: Parameterized Clipping Activation for Quantized Neural Networks
Choi, J.; Wang, Z.; Venkataramani, S.; Chuang, P. I.-J.; Srinivasan, V.; and Gopalakrishnan, K. 2018 · 2018
Cited alongside, same era.
Bi-real net: Enhancing the performance of 1-bit cnns with improved representational capability and advanced training algorithm
Liu, Z.; Wu, B.; Luo, W.; Yang, X.; Liu, W.; and Cheng, K.-T. 2018 · 2018
Cited alongside, same era.
Value-Aware Quantization for Training and Inference of Neural Networks
Park, E.; Yoo, S.; and Vajda, P. 2018 · 2018
Cited alongside, same era.
Mobilenetv2: Inverted Residuals and Linear Bottlenecks
Sandler, M.; Howard, A.; Zhu, M.; Zhmoginov, A.; and Chen, L.-C. 2018 · 2018
Cited alongside, same era.
Post training 4-bit quantization of convolutional networks for rapid-deployment
Banner, R.; Nahshan, Y.; and Soudry, D. 2019 · 2019
Cited alongside, same era.
Metaquant: Learning to Quantize by Learning to Penetrate Non-Differentiable Quantization
Chen, S.; Wang, W.; and Pan, S. J. 2019 · 2019
Cited alongside, same era.
Cai, Y.; Yao, Z.; Dong, Z.; Gholami, A.; Mahoney, M. W.; and Keutzer, K. 2020 · 2020
Later among the works it cites.
One Weight Bitwidth to Rule Them All
Chin, T.-W.; Pierce, I.; Chuang, J.; Chandra, V.; and Marculescu, D. 2020 · 2020
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HAWQ-V2: Hessian Aware Trace-Weighted Quantization of Neural Networks
Dong, Z.; Yao, Z.; Arfeen, D.; Gholami, A.; Mahoney, M. W.; and Keutzer, K. 2020 · 2020
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Single Path One-Shot Neural Architecture Search with Uniform Sampling
Guo, Z.; Zhang, X.; Mu, H.; Heng, W.; Liu, Z.; Wei, Y.; and Sun, J. 2020 · 2020
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An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
Dosovitskiy, A.; Beyer, L.; Kolesnikov, A.; Weissenborn, D.; Zhai, X.; Unterthiner, T.; Dehghani, M.; Minderer, M.; Heigold, G.; Gelly, S.; Uszkoreit, J.; and Houlsby, N. 2021 · 2021
Closest in time.
{BRECQ}: Pushing the Limit of Post-Training Quantization by Block Reconstruction
Li, Y.; Gong, R.; Tan, X.; Yang, Y.; Hu, P.; Zhang, Q.; Yu, F.; Wang, W.; and Gu, S. 2021 · 2021
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FracBits: Mixed Precision Quantization via Fractional Bit-Widths
Yang, L.; and Jin, Q. 2021 · 2021
Closest in time.
HAWQ-V3: Dyadic Neural Network Quantization
Yao, Z.; Dong, Z.; Zheng, Z.; Gholami, A.; Yu, J.; Tan, E.; Wang, L.; Huang, Q.; Wang, Y.; Mahoney, M.; et al. 2021 · 2021
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