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Generative data-free quantization emerges as a practical compression approach that quantizes deep neural networks to low bit-width without accessing the real data.
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Kernel learning by unconstrained optimization
Fuxin Li, Yunshan Fu, Yu-Hong Dai, Cristian Sminchisescu, and Jue Wang · 2009
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Determinantal point processes for machine learning
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Deep learning with limited numerical precision
Suyog Gupta, Ankur Agrawal, Kailash Gopalakrishnan, and Pritish Narayanan · 2015
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Inceptionism: Going deeper into neural networks
A. Mordvintsev, Christopher Olah, and M. Tyka · 2015
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Resiliency of deep neural networks under quantization
Wonyong Sung, Sungho Shin, and Kyuyeon Hwang · 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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Deep residual learning for image recognition
K. He, X. Zhang, S. Ren, and J. Sun · 2016
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Faster r-cnn: Towards real-time object detection with region proposal networks, 2016
Shaoqing Ren, Kaiming He, Ross Girshick, and Jian Sun · 2016
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Rethinking the inception architecture for computer vision
Christian Szegedy, Vincent Vanhoucke, Sergey Ioffe, Jon Shlens, and Zbigniew Wojna · 2016
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Imagenet classification with deep convolutional neural networks
Krizhevsky, Alex, Sutskever, Ilya, Hinton, and E. Geoffrey · 2017
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Mmd gan: Towards deeper understanding of moment matching network
Chun-Liang Li, Wei-Cheng Chang, Yu Cheng, Yiming Yang, and Barnabás Póczos · 2017
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Conditional image synthesis with auxiliary classifier gans
Augustus Odena, Christopher Olah, and Jonathon Shlens · 2017
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Post training 4-bit quantization of convolution networks for rapid-deployment
R Banner, Y Nahshan, E Hoffer, and D Soudry · 2018
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Fast greedy map inference for determinantal point process to improve recommendation diversity
Laming Chen, Guoxin Zhang, and Hanning Zhou · 2018
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Amir Gholami, Kiseok Kwon, Bichen Wu, Zizheng Tai, Xiangyu Yue, Peter Jin, Sicheng Zhao, and Kurt Keutzer · 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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The knowledge within: Methods for data-free model compression
M. Haroush, I. Hubara, E. Hoffer, and D. Soudry · 2020
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Up or down? adaptive rounding for post-training quantization, 2020
Markus Nagel, Rana Ali Amjad, Mart van Baalen, Christos Louizos, and Tijmen Blankevoort · 2020
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Bipointnet: Binary neural network for point clouds, 2020
Haotong Qin, Zhongang Cai, Mingyuan Zhang, Yifu Ding, Haiyu Zhao, Shuai Yi, Xianglong Liu, and Hao Su · 2020
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Haotong Qin, Ruihao Gong, Xianglong Liu, Xiao Bai, Jingkuan Song, and Nicu Sebe · 2020
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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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Generative low-bitwidth data free quantization
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Value-aware quantization for training and inference of neural networks, 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
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Shufflenet: An extremely efficient convolutional neural network for mobile devices
Xiangyu Zhang, Xinyu Zhou, Mengxiao Lin, and Jian Sun · 2018
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Post-training 4-bit quantization of convolution networks for rapid-deployment, 2019
Ron Banner, Yury Nahshan, Elad Hoffer, 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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Learning deep binary descriptor with multi-quantization
Yueqi Duan, Jiwen Lu, Ziwei Wang, Jianjiang Feng, and Jie Zhou · 2019
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Gdpp: Learning diverse generations using determinantal point processes
Mohamed Elfeki, Camille Couprie, Morgane Riviere, and Mohamed Elhoseiny · 2019
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Xu Shoukai, Li Haokun, Zhuang Bohan, Liu Jing, Cao Jiezhang, Liang Chuangrun, and Tan Mingkui · 2020
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Towards efficient u-nets: A coupled and quantized approach
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Deep neural network compression by in-parallel pruning-quantization
Frederick Tung and Greg Mori · 2020
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Learning channel-wise interactions for binary convolutional neural networks
Ziwei Wang, Jiwen Lu, and Jie Zhou · 2020
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Dreaming to distill: Data-free knowledge transfer via deepinversion, 2020
Hongxu Yin, Pavlo Molchanov, Zhizhong Li, Jose M. Alvarez, Arun Mallya, Derek Hoiem, Niraj K. Jha, and Jan Kautz · 2020
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Zero-shot learning of a conditional generative adversarial network for data-free network quantization
Yoojin Choi, Mostafa El-Khamy, and Jungwon Lee · 2021
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Generative zero-shot network quantization
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Brecq: Pushing the limit of post-training quantization by block reconstruction
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Random features for kernel approximation: A survey on algorithms, theory, and beyond
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Discrimination-aware network pruning for deep model compression
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Zero-shot adversarial quantization
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Learning efficient binarized object detectors with information compression
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Diversifying sample generation for accurate data-free quantization
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Effective training of convolutional neural networks with low-bitwidth weights and activations
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SQuant: On-the-fly data-free quantization via diagonal hessian approximation
Cong Guo, Yuxian Qiu, Jingwen Leng, Xiaotian Gao, Chen Zhang, Yunxin Liu, Fan Yang, Yuhao Zhu, and Minyi Guo · 2022
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