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Quantized neural networks with low-bit weights and activations are attractive for developing AI accelerators.
Imagenet: A large-scale hierarchical image database
Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei · 2009
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Learning multiple layers of features from tiny images
Alex Krizhevsky and Geoffrey Hinton · 2009
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Low-complexity single-image super-resolution based on nonnegative neighbor embedding
Marco Bevilacqua, Aline Roumy, Christine Guillemot, and Marie Line Alberi-Morel · 2012
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Exploiting linear structure within convolutional networks for efficient evaluation
Emily Denton, Wojciech Zaremba, Joan Bruna, Yann LeCun, and Rob Fergus · 2014
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Speeding up convolutional neural networks with low rank expansions
Max Jaderberg, Andrea Vedaldi, and Andrew Zisserman · 2014
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Binaryconnect: training deep neural networks with binary weights during propagations
Matthieu Courbariaux, Yoshua Bengio, and Jean-Pierre David · 2015
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Fast r-cnn
Ross Girshick · 2015
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Delving deep into rectifiers: Surpassing human-level performance on imagenet classification
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2015
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Batch normalization: Accelerating deep network training by reducing internal covariate shift
Sergey Ioffe and Christian Szegedy · 2015
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Fast and accurate image upscaling with super-resolution forests
Samuel Schulter, Christian Leistner, and Horst Bischof · 2015
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Very deep convolutional networks for large-scale image recognition
Karen Simonyan and Andrew Zisserman · 2015
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Deep compression: Compressing deep neural networks with pruning, trained quantization and huffman coding
Song Han, Huizi Mao, and William J. Dally · 2016
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Binarized neural networks
Itay Hubara, Matthieu Courbariaux, Daniel Soudry, Ran El-Yaniv, and Yoshua Bengio · 2016
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Perceptual losses for real-time style transfer and super-resolution
Justin Johnson, Alexandre Alahi, and Li Fei-Fei · 2016
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Accurate image super-resolution using very deep convolutional networks
Jiwon Kim, Jung Kwon Lee, and Kyoung Mu Lee · 2016
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Fixed point quantization of deep convolutional networks
Darryl D. Lin, Sachin S. Talathi, and V. Sreekanth Annapureddy · 2016
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Xnor-net: Imagenet classification using binary convolutional neural networks
Mohammad Rastegari, Vicente Ordonez, Joseph Redmon, and Ali Farhadi · 2016
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You only look once: Unified, real-time object detection
Joseph Redmon, Santosh Kumar Divvala, Ross B. Girshick, and Ali Farhadi · 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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Deep learning with low precision by half-wave gaussian quantization
Zhaowei Cai, Xiaodong He, Jian Sun, and Nuno Vasconcelos · 2017
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Mask r-cnn
Kaiming He, Georgia Gkioxari, Piotr Dollár, and Ross Girshick · 2017
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Channel pruning for accelerating very deep neural networks
Yihui He, Xiangyu Zhang, and Jian Sun · 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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Performance guaranteed network acceleration via high-order residual quantization
Zefan Li, Bingbing Ni, Wenjun Zhang, Xiaokang Yang, and Wen Gao · 2017
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Towards accurate binary convolutional neural network
Xiaofan Lin, Cong Zhao, and Wei Pan · 2017
Structured binary neural networks for accurate image classification and semantic segmentation
Bohan Zhuang, Chunhua Shen, Mingkui Tan, Lingqiao Liu, and Ian D. Reid · 2018
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Proximal mean-field for neural network quantization
Thalaiyasingam Ajanthan, Puneet Dokania, Richard Hartley, and Philip Torr · 2019
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Back to simplicity: How to train accurate bnns from scratch?
Joseph Bethge, Haojin Yang, Marvin Bornstein, and Christoph Meinel · 2019
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Regularizing activation distribution for training binarized deep networks
Ruizhou Ding, Ting-Wu Chin, Zeye Liu, and Diana Marculescu · 2019
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Differentiable soft quantization: Bridging full-precision and low-bit neural networks
Ruihao Gong, Xianglong Liu, Shenghu Jiang, Tianxiang Li, Peng Hu, Jiazhen Lin, Fengwei Yu, and Junjie Yan · 2019
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Sgdr: Stochastic gradient descent with warm restarts
Ilya Loshchilov and Frank Hutter · 2017
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On compressing deep models by low rank and sparse decomposition
Xiyu Yu, Tongliang Liu, Xinchao Wang, and Dacheng Tao · 2017
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Trained ternary quantization
Chenzhuo Zhu, Song Han, Huizi Mao, and William J. Dally · 2017
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Pact: Parameterized clipping activation for quantized neural networks
Jungwook Choi · 2018
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Syq: Learning symmetric quantization for efficient deep neural networks
Julian Faraone, Nicholas Fraser, Michaela Blott, and Philip H.W. Leong · 2018
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Projection convolutional neural networks for 1-bit cnns via discrete back propagation
Jiaxin Gu, Ce Li, Baochang Zhang, Jungong Han, Xianbin Cao, Jianzhuang Liu, and David Doermann · 2018
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Ruihao Gong, Xianglong Liu, Shenghu Jiang, Tianxiang Li, Peng Hu, Jiazhen Lin, Fengwei Yu, and Junjie Yan · 2019
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Bayesian optimized 1-bit cnns
Jiaxin Gu, Junhe Zhao, Xiaolong Jiang, Baochang Zhang, Liu Jianzhuang, Guodong Guo, and Rongrong Ji · 2019
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Circulant binary convolutional networks: Enhancing the performance of 1-bit dcnns with circulant back propagation
Chunlei Liu, Wenrui Ding, Xin Xia, Baochang Zhang, Jiaxin Gu, Jianzhuang Liu, Rongrong Ji, and David Doermann · 2019
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Darts: Differentiable architecture search
Hanxiao Liu, Karen Simonyan, and Yiming Yang · 2019
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Mobinet: A mobile binary network for image classification
Hai Phan, Dang Huynh, Yihui He, Marios Savvides, and Zhiqiang Shen · 2019
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Balanced binary neural networks with gated residual
Mingzhu Shen, Xianglong Liu, Kai Han, Ruihao Gong, Yunhe Wang, and Chang Xu · 2019
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Learning channel-wise interactions for binary convolutional neural networks
Ziwei Wang, Jiwen Lu, Chenxin Tao, Jie Zhou, and Qi Tian · 2019
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Fbnet: Hardware-aware efficient convnet design via differentiable neural architecture search
Bichen Wu, Xiaoliang Dai, Peizhao Zhang, Yanghan Wang, Fei Sun, Yiming Wu, Yuandong Tian, Peter Vajda, Yangqing Jia, and Kurt Keutzer · 2019
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Snas: stochastic neural architecture search
Sirui Xie, Hehui Zheng, Chunxiao Liu, and Liang Lin · 2019
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Legonet: Efficient convolutional neural networks with lego filters
Zhaohui Yang, Yunhe Wang, Chuanjian Liu, Hanting Chen, Chunjing Xu, Boxin Shi, Chao Xu, and Chang Xu · 2019
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Autoslim: Towards one-shot architecture search for channel numbers
Jiahui Yu and Thomas Huang · 2019
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Binarized neural networks for resource-efficient hashing with minimizing quantization loss
Feng Zheng, Cheng Deng, and Heng Huang · 2019
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Milenas: Efficient neural architecture search via mixed-level reformulation
Chaoyang He, Haishan Ye, Li Shen, and Tong Zhang · 2020
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Bignas: Scaling up neural architecture search with big single-stage models
Jiahui Yu, Pengchong Jin, Hanxiao Liu, Gabriel Bender, Pieter-Jan Kindermans, Mingxing Tan, Thomas S. Huang, Xiaodan Song, Ruoming Pang, and Quoc Le · 2020
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Cogradient descent for bilinear optimization
Li’an Zhuo, Baochang Zhang, Linlin Yang, Hanlin Chen, Qixiang Ye, David Doermann, Rongrong Ji, and Guodong Guo · 2020
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