Pruning

AMC: Automated Model Compression and Acceleration with Reinforcement Learning
Yihui He, Xi'an Jiaotong University; Ji Lin, Tsinghua University; Song Han*, MIT

Extreme Network Compression via Filter Group Approximation
Bo Peng*, Hikvision Research Institute; Wenming Tan, Hikvision Research Institute; Zheyang Li, Hikvision Research Institute; Shun Zhang, Hikvision Research Institute; Di Xie, Hikvision Research Institute; Shiliang Pu, Hikvision Research Institute

Clustering Kernels for Compressing the Convolutional Neural Networks
Sanghyun Son, Seoul National University; Seungjun Nah, Seoul National University; Kyoung Mu Lee*, Seoul National University

Constraints Matter in Deep Neural Network Compression
Changan Chen, Simon Fraser University; Fred Tung*, Simon Fraser University; Naveen Vedula, Simon Fraser University; Greg Mori, Simon Fraser University

Coreset-Based Convolutional Neural Network Compression
Abhimanyu Dubey*, Massachusetts Institute of Technology; Moitreya Chatterjee, University of Illinois at Urbana Champaign; Ramesh Raskar, Massachusetts Institute of Technology; Narendra Ahuja, University of Illinois at Urbana-Champaign, USA

A Systematic DNN Weight Pruning Framework using Alternating Direction Method of Multipliers
Tianyun Zhang*, Syracuse University; Shaokai Ye, Syracuse University; Kaiqi Zhang, Syracuse University; Yanzhi Wang, Syracuse University; Makan Fardad, Syracuse Universtiy; Wujie Wen, Florida International University

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Low-rank

Constrained Optimization Based Low-Rank Approximation of Deep Neural Networks
Chong Li*, University of Washington; C.J. Richard Shi, University of Washington

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KD

Self-supervised Knowledge Distillation Using Singular Value Decomposition

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low-Bit

Training Binary Weight Networks via Semi-Binary Decomposition
Qinghao Hu*, Institute of Automation, Chinese Academy of Sciences; Gang Li, Institute of Automation, Chinese Academy of Sciences; Peisong Wang, Institute of Automation, Chinese Academy of Sciences; yifan zhang, Institute of Automation,Chinese Academy of Sciences; Jian Cheng, Chinese Academy of Sciences, China

Bi-Real Net: Enhancing the Performance of 1-bit CNNs with Improved Representational Capability and Advanced Training Algorithm
zechun liu*, HKUST; Baoyuan Wu, Tencent AI Lab; Wenhan Luo, Tencent AI Lab; Xin Yang, Huazhong University of Science and Technology; Wei Liu, Tencent AI Lab; Kwang-Ting Cheng, Hong Kong University of Science and Technology

Optimized Quantization for Highly Accurate and Compact DNNs
Dongqing Zhang, Microsoft Research; Jiaolong Yang*, Microsoft Research Asia (MSRA); Dongqiangzi Ye, Microsoft Research; Gang Hua, Microsoft Cloud and AI

Quantized Densely Connected U-Nets for Efficient Landmark Localization
Zhiqiang Tang*, Rutgers; Xi Peng, Rutgers University; Shijie Geng, Rutgers; Shaoting Zhang, University of North Carolina at Charlotte; Lingfei Wu, IBM T. J. Watson Research Center; Dimitris Metaxas, Rutgers

Value-aware Quantization for Training and Inference of Neural Networks
Eunhyeok Park, Seoul National University; Sungjoo Yoo*, Seoul National University; Peter Vajda, Facebook

Quantization Mimic: Towards Very Tiny CNN for Object Detection
Yi Wei*, Tsinghua University; Xinyu Pan, MMLAB, CUHK; Hongwei Qin, SenseTime; Junjie Yan, Sensetime; Wanli Ouyang, CUHK

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Compact Network

Light-weight CNN Architecture Design for Fast Inference
Ningning Ma*, Tsinghua; Xiangyu Zhang, Megvii Inc; Hai-Tao Zheng, Tsinghua University; Jian Sun, Megvii, Face++

Deep Expander Networks: Efficient Deep Networks from Graph Theory
Ameya Prabhu*, IIIT Hyderabad; Girish Varma, IIIT Hyderabad; Anoop Namboodiri, IIIT Hyderbad

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近几年其他相关文章可以查询
http://www.sohu.com/a/232047203_473283
https://github.com/ZhishengWang/Embedded-Neural-Network