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Quantization is of significance for compressing the over-parameterized deep neural models and deploying them on resource-limited devices.
The information bottleneck method
Naftali Tishby, Fernando C Pereira, and William Bialek · 2000
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Learning multiple layers of features from tiny images
Alex Krizhevsky, Geoffrey Hinton, et al · 2009
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Cats and dogs
Omkar M Parkhi, Andrea Vedaldi, Andrew Zisserman, and CV Jawahar · 2012
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Estimating or Propagating Gradients Through Stochastic Neurons for Conditional Computation
Yoshua Bengio, Nicholas Léonard, and Aaron C. Courville · 2013
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Song Han, Huizi Mao, and William J Dally · 2015
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Faster r-cnn: Towards real-time object detection with region proposal networks
Shaoqing Ren, Kaiming He, Ross Girshick, and Jian Sun · 2015
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Deep learning and the information bottleneck principle
Naftali Tishby and Noga Zaslavsky · 2015
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Deep Residual Learning for Image Recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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DoReFa-Net: Training Low Bitwidth Convolutional Neural Networks with Low Bitwidth Gradients
Shuchang Zhou, Zekun Ni, Xinyu Zhou, He Wen, Yuxin Wu, and Yuheng Zou · 2016
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Neural architecture search with reinforcement learning
Barret Zoph and Quoc V Le · 2016
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Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs
Liang-Chieh Chen, George Papandreou, Iasonas Kokkinos, Kevin Murphy, and Alan L Yuille · 2017
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Mask r-cnn
Kaiming He, Georgia Gkioxari, Piotr Dollár, and Ross Girshick · 2017
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MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications
Andrew G. Howard, Menglong Zhu, Bo Chen, Dmitry Kalenichenko, Weijun Wang, Tobias Weyand, Marco Andreetto, and Hartwig Adam · 2017
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Runtime neural pruning
Ji Lin, Yongming Rao, Jiwen Lu, and Jie Zhou · 2017
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PACT: Parameterized Clipping Activation for Quantized Neural Networks
Jungwook Choi, Zhuo Wang, Swagath Venkataramani, Pierce I-Jen Chuang, Vijayalakshmi Srinivasan, and Kailash Gopalakrishnan · 2018
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Darts: Differentiable architecture search
Hanxiao Liu, Karen Simonyan, and Yiming Yang · 2018
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Efficient neural architecture search via parameters sharing
Hieu Pham, Melody Guan, Barret Zoph, Quoc Le, and Jeff Dean · 2018
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Model compression via distillation and quantization
Antonio Polino, Razvan Pascanu, and Dan Alistarh · 2018
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Yolov3: An incremental improvement
Joseph Redmon and Ali Farhadi · 2018
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MobileNetV2: Inverted Residuals and Linear Bottlenecks
Mark Sandler, Andrew G. Howard, Menglong Zhu, Andrey Zhmoginov, and Liang-Chieh Chen · 2018
Cited alongside, same era.
Skipnet: Learning dynamic routing in convolutional networks
Xin Wang, Fisher Yu, Zi-Yi Dou, Trevor Darrell, and Joseph E Gonzalez · 2018
Cited alongside, same era.
Mixed Precision Quantization of ConvNets via Differentiable Neural Architecture Search
Bichen Wu, Yanghan Wang, Peizhao Zhang, Yuandong Tian, Peter Vajda, and Kurt Keutzer · 2018
Cited alongside, same era.
Adaptive quantization for deep neural network
Yiren Zhou, Seyed-Mohsen Moosavi-Dezfooli, Ngai-Man Cheung, and Pascal Frossard · 2018
Cited alongside, same era.
HAWQ: Hessian AWare Quantization of Neural Networks With Mixed-precision
Zhen Dong, Zhewei Yao, Amir Gholami, Michael W. Mahoney, and Kurt Keutzer · 2019
Cited alongside, same era.
Differentiable Soft Quantization: Bridging Full-precision and Low-bit Neural Networks
Adabits: Neural network quantization with adaptive bit-widths
Qing Jin, Linjie Yang, and Zhenyu Liao · 2020
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Up or down? adaptive rounding for post-training quantization
Markus Nagel, Rana Ali Amjad, Mart Van Baalen, Christos Louizos, and Tijmen Blankevoort · 2020
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Fractional skipping: Towards finer-grained dynamic cnn inference
Jianghao Shen, Yue Wang, Pengfei Xu, Yonggan Fu, Zhangyang Wang, and Yingyan Lin · 2020
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Efficientdet: Scalable and efficient object detection
Mingxing Tan, Ruoming Pang, and Quoc V Le · 2020
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Bayesian bits: Unifying quantization and pruning
Mart Van Baalen, Christos Louizos, Markus Nagel, Rana Ali Amjad, Ying Wang, Tijmen Blankevoort, and Max Welling · 2020
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Dynamic network pruning with interpretable layerwise channel selection
Yulong Wang, Xiaolu Zhang, Xiaolin Hu, Bo Zhang, and Hang Su · 2020
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Ruihao Gong, Xianglong Liu, Shenghu Jiang, Tianxiang Li, Peng Hu, Jiazhen Lin, Fengwei Yu, and Junjie Yan · 2019
Cited alongside, same era.
Searching for MobileNetV3
Andrew Howard, Ruoming Pang, Hartwig Adam, Quoc V. Le, Mark Sandler, Bo Chen, Weijun Wang, Liang-Chieh Chen, Mingxing Tan, Grace Chu, Vijay Vasudevan, and Yukun Zhu · 2019
Cited alongside, same era.
Gpipe: Efficient training of giant neural networks using pipeline parallelism
Yanping Huang, Youlong Cheng, Ankur Bapna, Orhan Firat, Dehao Chen, Mia Chen, HyoukJoong Lee, Jiquan Ngiam, Quoc V Le, Yonghui Wu, et al · 2019
Cited alongside, same era.
Rethinking the Value of Network Pruning
Zhuang Liu, Mingjie Sun, Tinghui Zhou, Gao Huang, and Trevor Darrell · 2019
Cited alongside, same era.
Importance estimation for neural network pruning
Pavlo Molchanov, Arun Mallya, Stephen Tyree, Iuri Frosio, and Jan Kautz · 2019
Cited alongside, same era.
Efficientnet: Rethinking model scaling for convolutional neural networks
Mingxing Tan and Quoc Le · 2019
Cited alongside, same era.
Mixed precision dnns: All you need is a good parametrization
Stefan Uhlich, Lukas Mauch, Fabien Cardinaux, Kazuki Yoshiyama, Javier Alonso Garcia, Stephen Tiedemann, Thomas Kemp, and Akira Nakamura · 2019
Cited alongside, same era.
Later among the works it cites.
Bit-mixer: Mixed-precision networks with runtime bit-width selection
Adrian Bulat and Georgios Tzimiropoulos · 2021
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Towards Mixed-precision Quantization of Neural Networks via Constrained Optimization
Weihan Chen, Peisong Wang, and Jian Cheng · 2021
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Accurate post training quantization with small calibration sets
Itay Hubara, Yury Nahshan, Yair Hanani, Ron Banner, and Daniel Soudry · 2021
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Network quantization with element-wise gradient scaling
Junghyup Lee, Dohyung Kim, and Bumsub Ham · 2021
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FracBits: Mixed Precision Quantization via Fractional Bit-widths
Linjie Yang and Qing Jin · 2021
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bert2bert: Towards reusable pretrained language models
Cheng Chen, Yichun Yin, Lifeng Shang, Xin Jiang, Yujia Qin, Fengyu Wang, Zhi Wang, Xiao Chen, Zhiyuan Liu, and Qun Liu · 2022
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Differentiable model compression via pseudo quantization noise
Alexandre Défossez, Yossi Adi, and Gabriel Synnaeve · 2022
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SDQ: Stochastic Differentiable Quantization with Mixed Precision
Xijie Huang, Zhiqiang Shen, Shichao Li, Zechun Liu, Xianghong Hu, Jeffry Wicaksana, Eric P. Xing, and Kwang-Ting Cheng · 2022
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Overcoming oscillations in quantization-aware training
Markus Nagel, Marios Fournarakis, Yelysei Bondarenko, and Tijmen Blankevoort · 2022
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Skipbert: Efficient inference with shallow layer skipping
Jue Wang, Ke Chen, Gang Chen, Lidan Shou, and Julian McAuley · 2022
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Rongwei Lu, Yutong Jiang, Yinan Mao, Chen Tang, Bin Chen, Laizhong Cui, and Zhi Wang · 2023
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Qbitopt: Fast and accurate bitwidth reallocation during training
Jorn Peters, Marios Fournarakis, Markus Nagel, Mart van Baalen, and Tijmen Blankevoort · 2023
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Nipq: Noise proxy-based integrated pseudo-quantization
Juncheol Shin, Junhyuk So, Sein Park, Seungyeop Kang, Sungjoo Yoo, and Eunhyeok Park · 2023
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