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Recently, post-training quantization (PTQ) has driven much attention to produce efficient neural networks without long-time retraining.
Flat minima
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Learning multiple layers of features from tiny images
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Song Han, Huizi Mao, and William J Dally · 2015
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Distilling the knowledge in a neural network
Geoffrey Hinton, Oriol Vinyals, and Jeff Dean · 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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ImageNet Large Scale Visual Recognition Challenge
Olga Russakovsky, Jia Deng, Hao Su, Jonathan Krause, Sanjeev Satheesh, Sean Ma, Zhiheng Huang, Andrej Karpathy, Aditya Khosla, Michael Bernstein, Alexander C. Berg, and Li Fei-Fei · 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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On large-batch training for deep learning: Generalization gap and sharp minima
Nitish Shirish Keskar, Dheevatsa Mudigere, Jorge Nocedal, Mikhail Smelyanskiy, and Ping Tak Peter Tang · 2016
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Neural architecture search with reinforcement learning
Barret Zoph and Quoc V Le · 2016
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Sharp minima can generalize for deep nets, 2017
Laurent Dinh, Razvan Pascanu, Samy Bengio, and Yoshua Bengio · 2017
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Focal loss for dense object detection
Tsung-Yi Lin, Priya Goyal, Ross Girshick, Kaiming He, and Piotr Dollár · 2017
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Exploring generalization in deep learning, 2017
Behnam Neyshabur, Srinadh Bhojanapalli, David McAllester, and Nathan Srebro · 2017
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Bert: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova · 2018
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Averaging weights leads to wider optima and better generalization
Pavel Izmailov, Dmitrii Podoprikhin, Timur Garipov, Dmitry Vetrov, and Andrew Gordon Wilson · 2018
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Quantization and training of neural networks for efficient integer-arithmetic-only inference
Benoit Jacob, Skirmantas Kligys, Bo Chen, Menglong Zhu, Matthew Tang, Andrew Howard, Hartwig Adam, and Dmitry Kalenichenko · 2018
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Mobilenetv2: Inverted residuals and linear bottlenecks
Mark Sandler, Andrew Howard, Menglong Zhu, Andrey Zhmoginov, and Liang-Chieh Chen · 2018
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Post training 4-bit quantization of convolutional networks for rapid-deployment
Ron Banner, Yury Nahshan, and Daniel Soudry · 2019
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Low-bit quantization of neural networks for efficient inference
Yoni Choukroun, Eli Kravchik, Fan Yang, and Pavel Kisilev · 2019
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Hawq-v2: Hessian aware trace-weighted quantization of neural networks
Zhen Dong, Zhewei Yao, Yaohui Cai, Daiyaan Arfeen, Amir Gholami, Michael W Mahoney, and Kurt Keutzer · 2019
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Zeroq: A novel zero shot quantization framework
Yaohui Cai, Zhewei Yao, Zhen Dong, Amir Gholami, Michael W Mahoney, and Kurt Keutzer · 2020
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Training with quantization noise for extreme model compression
Angela Fan, Pierre Stock, Benjamin Graham, Edouard Grave, Rémi Gribonval, Herve Jegou, and Armand Joulin · 2020
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Sharpness-aware minimization for efficiently improving generalization
Pierre Foret, Ariel Kleiner, Hossein Mobahi, and Behnam Neyshabur · 2020
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Comparing fisher information regularization with distillation for dnn quantization
Prad Kadambi, Karthikeyan Natesan Ramamurthy, and Visar Berisha · 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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Steven K Esser, Jeffrey L McKinstry, Deepika Bablani, Rathinakumar Appuswamy, and Dharmendra S Modha · 2019
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Fantastic generalization measures and where to find them
Yiding Jiang, Behnam Neyshabur, Hossein Mobahi, Dilip Krishnan, and Samy Bengio · 2019
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Additive powers-of-two quantization: An efficient non-uniform discretization for neural networks
Yuhang Li, Xin Dong, and Wei Wang · 2019
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Data-free quantization through weight equalization and bias correction
Markus Nagel, Mart van Baalen, Tijmen Blankevoort, and Max Welling · 2019
Cited alongside, same era.
Loss aware post-training quantization
Yury Nahshan, Brian Chmiel, Chaim Baskin, Evgenii Zheltonozhskii, Ron Banner, Alex M Bronstein, and Avi Mendelson · 2019
Cited alongside, same era.
Pytorch: An imperative style, high-performance deep learning library
Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, et al · 2019
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Mnasnet: Platform-aware neural architecture search for mobile
Mingxing Tan, Bo Chen, Ruoming Pang, Vijay Vasudevan, Mark Sandler, Andrew Howard, and Quoc V Le · 2019
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Forward and backward information retention for accurate binary neural networks
Haotong Qin, Ruihao Gong, Xianglong Liu, Mingzhu Shen, Ziran Wei, Fengwei Yu, and Jingkuan Song · 2020
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Designing network design spaces
Ilija Radosavovic, Raj Prateek Kosaraju, Ross Girshick, Kaiming He, and Piotr Dollár · 2020
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Towards accurate post-training network quantization via bit-split and stitching
Peisong Wang, Qiang Chen, Xiangyu He, and Jian Cheng · 2020
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Adversarial weight perturbation helps robust generalization
Dongxian Wu, Shu-Tao Xia, and Yisen Wang · 2020
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Double-win quant: Aggressively winning robustness of quantized deep neural networks via random precision training and inference
Yonggan Fu, Qixuan Yu, Meng Li, Vikas Chandra, and Yingyan Lin · 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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Once quantization-aware training: High performance extremely low-bit architecture search
Mingzhu Shen, Feng Liang, Ruihao Gong, Yuhang Li, Chuming Li, Chen Lin, Fengwei Yu, Junjie Yan, and Wanli Ouyang · 2021
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Is in-domain data really needed? a pilot study on cross-domain calibration for network quantization
Haichao Yu, Linjie Yang, and Humphrey Shi · 2021
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Regularizing neural networks via adversarial model perturbation
Yaowei Zheng, Richong Zhang, and Yongyi Mao · 2021
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