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Quantization is wildly taken as a model compression technique, which obtains efficient models by converting floating-point weights and activations in the neural network into lower-bit integers.
Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
Earlier work this paper cites.
Incremental network quantization: Towards lossless CNNs with low-precision weights
Aojun Zhou, Anbang Yao, Yiwen Guo, Lin Xu, and Yurong Chen · 2017
Earlier work this paper cites.
Adaptive quantization for deep neural network
Yiren Zhou, Seyed-Mohsen Moosavi-Dezfooli, Ngai-Man Cheung, and Pascal Frossard · 2017
Earlier work this paper cites.
PACT: Parameterized clipping activation for quantized neural networks
Jungwook Choi, Zhuo Wang, Swagath Venkataramani, Pierce I-Jen Chuang, Vijayalakshmi Srinivasan, and Kailash Gopalakrishnan · 2018
Earlier work this paper cites.
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
Earlier work this paper cites.
Value-aware quantization for training and inference of neural networks
Eunhyeok Park, Sungjoo Yoo, and Peter Vajda · 2018
Earlier work this paper cites.
LQ-Nets: Learned quantization for highly accurate and compact deep neural networks
Dongqing Zhang, Jiaolong Yang, Dongqiangzi Ye, and Gang Hua · 2018
Earlier work this paper cites.
HAWQ: Hessian AWare Quantization of neural networks with mixed-precision
Zhen Dong, Zhewei Yao, Amir Gholami, Michael W. Mahoney, and Kurt Keutzer · 2019
Earlier work this paper cites.
Searching for MobileNetV3
Andrew Howard, Mark Sandler, Grace Chu, Liang-Chieh Chen, Bo Chen, Mingxing Tan, Weijun Wang, Yukun Zhu, Ruoming Pang, Vijay Vasudevan, et al · 2019
Earlier work this paper cites.
Fully quantized network for object detection
Rundong Li, Yan Wang, Feng Liang, Hongwei Qin, Junjie Yan, and Rui Fan · 2019
Earlier work this paper cites.
Efficientnet: Rethinking model scaling for convolutional neural networks
Mingxing Tan and Quoc V Le · 2019
Earlier work this paper cites.
HAQ: Hardware-aware automated quantization
Kuan Wang, Zhijian Liu, Yujun Lin, Ji Lin, and Song Han · 2019
Cited alongside, same era.
https://github.com/rwightman/pytorch-image-models-, 2019
Ross Wightman · 2019
Cited alongside, same era.
ZeroQ: A novel zero shot quantization framework
Yaohui Cai, Zhewei Yao, Zhen Dong, Amir Gholami, Michael W. Mahoney, and Kurt Keutzer · 2020
Cited alongside, same era.
HAWQ-V2: Hessian aware trace-weighted quantization of neural networks
Zhen Dong, Zhewei Yao, Daiyaan Arfeen, Amir Gholami, Michael W. Mahoney, and Kurt Keutzer · 2020
Cited alongside, same era.
An image is worth 16x16 words: Transformers for image recognition at scale
Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, et al · 2020
Cited alongside, same era.
Pay attention to mlps
Hanxiao Liu, Zihang Dai, David So, and Quoc Le · 2021
Later among the works it cites.
Swin transformer: Hierarchical vision transformer using shifted windows
Ze Liu, Yutong Lin, Yue Cao, Han Hu, Yixuan Wei, Zheng Zhang, Stephen Lin, and Baining Guo · 2021
Later among the works it cites.
Post-training quantization for vision transformer
Zhenhua Liu, Yunhe Wang, Kai Han, Wei Zhang, Siwei Ma, and Wen Gao · 2021
Later among the works it cites.
Mlp-mixer: An all-mlp architecture for vision
Ilya O Tolstikhin, Neil Houlsby, Alexander Kolesnikov, Lucas Beyer, Xiaohua Zhai, Thomas Unterthiner, Jessica Yung, Andreas Steiner, Daniel Keysers, Jakob Uszkoreit, et al · 2021
Later among the works it cites.
Resmlp: Feedforward networks for image classification with data-efficient training
Hugo Touvron, Piotr Bojanowski, Mathilde Caron, Matthieu Cord, Alaaeldin El-Nouby, Edouard Grave, Gautier Izacard, Armand Joulin, Gabriel Synnaeve, Jakob Verbeek, et al · 2021
Later among the works it cites.
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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
Cited alongside, same era.
Easyquant: Post-training quantization via scale optimization
Di Wu, Qi Tang, Yongle Zhao, Ming Zhang, Ying Fu, and Debing Zhang · 2020
Cited alongside, same era.
Pyhessian: Neural networks through the lens of the hessian
Zhewei Yao, Amir Gholami, Kurt Keutzer, and Michael W Mahoney · 2020
Cited alongside, same era.
Hao: Hardware-aware neural architecture optimization for efficient inference
Zhen Dong, Yizhao Gao, Qijing Huang, John Wawrzynek, Hayden KH So, and Kurt Keutzer · 2021
Cited alongside, same era.
A survey of quantization methods for efficient neural network inference
Amir Gholami, Sehoon Kim, Zhen Dong, Zhewei Yao, Michael W Mahoney, and Kurt Keutzer · 2021
Cited alongside, same era.
Codenet: Efficient deployment of input-adaptive object detection on embedded fpgas
Qijing Huang, Dequan Wang, Zhen Dong, Yizhao Gao, Yaohui Cai, Tian Li, Bichen Wu, Kurt Keutzer, and John Wawrzynek · 2021
Cited alongside, same era.
Hawq-v3: Dyadic neural network quantization
Zhewei Yao, Zhen Dong, Zhangcheng Zheng, Amir Gholami, Jiali Yu, Eric Tan, Leyuan Wang, Qijing Huang, Yida Wang, Michael Mahoney, et al · 2021
Later among the works it cites.
Rethinking token-mixing mlp for mlp-based vision backbone
Tan Yu, Xu Li, Yunfeng Cai, Mingming Sun, and Ping Li · 2021
Later among the works it cites.
pnlp-mixer: an efficient all-mlp architecture for language
Francesco Fusco, Damian Pascual, and Peter Staar · 2022
Closest in time.
Rethinking network design and local geometry in point cloud: A simple residual mlp framework
Xu Ma, Can Qin, Haoxuan You, Haoxi Ran, and Yun Fu · 2022
Closest in time.
Asher Trockman and J Zico Kolter · 2022
Closest in time.
S2-mlp: Spatial-shift mlp architecture for vision
Tan Yu, Xu Li, Yunfeng Cai, Mingming Sun, and Ping Li · 2022
Closest in time.