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Pruning is a widely used technique for reducing the size of deep neural networks while maintaining their performance.
Optimal brain damage
Yann LeCun, John S Denker, and Sara A Solla · 1990
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Second order derivatives for network pruning: Optimal brain surgeon
Babak Hassibi and David G Stork · 1993
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Learning both weights and connections for efficient neural network
Song Han, Jeff Pool, John Tran, and William Dally · 2015
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Pruning filters for efficient convnets
Hao Li, Asim Kadav, Igor Durdanovic, Hanan Samet, and Hans Peter Graf · 2016
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An entropy-based pruning method for cnn compression, 2017
Jian-Hao Luo and Jianxin Wu · 2017
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Variational dropout and the local reparameterization trick
Dmitry Molchanov, Stephen Tyree, Tero Karras, and Timo Aila · 2017
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2pfpce: Two-phase filter pruning based on conditional entropy, 2018
Chuhan Min, Aosen Wang, Yiran Chen, Wenyao Xu, and Xin Chen · 2018
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The lottery ticket hypothesis: Finding sparse, trainable neural networks
Jonathan Frankle and Michael Carbin · 2019
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Entropy-based pruning method for convolutional neural networks
Cheonghwan Hur and Sanggil Kang · 2019
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Quantization networks
Jiwei Yang, Xu Shen, Jun Xing, Xinmei Tian, Houqiang Li, Bing Deng, Jianqiang Huang, and Xian-sheng Hua · 2019
Cited alongside, same era.
The lottery ticket hypothesis for pre-trained bert networks
Tianlong Chen, Jonathan Frankle, Shiyu Chang, Sijia Liu, Yang Zhang, Zhangyang Wang, and Michael Carbin · 2020
Cited alongside, same era.
Model compression and hardware acceleration for neural networks: A comprehensive survey
Lei Deng, Guoqi Li, Song Han, Luping Shi, and Yuan Xie · 2020
Cited alongside, same era.
On the role of structured pruning for neural network compression
Andrea Bragagnolo, Enzo Tartaglione, Attilio Fiandrotti, and Marco Grangetto · 2021
Cited alongside, same era.
The lottery tickets hypothesis for supervised and self-supervised pre-training in computer vision models
Tianlong Chen, Jonathan Frankle, Shiyu Chang, Sijia Liu, Yang Zhang, Michael Carbin, and Zhangyang Wang · 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 · 2022
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Sparse double descent: Where network pruning aggravates overfitting
Zheng He, Zeke Xie, Quanzhi Zhu, and Zengchang Qin · 2022
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Revisiting random channel pruning for neural network compression
Yawei Li, Kamil Adamczewski, Wen Li, Shuhang Gu, Radu Timofte, and Luc Van Gool · 2022
Later among the works it cites.
Towards efficient capsule networks
Riccardo Renzulli and Marco Grangetto · 2022
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The rise of the lottery heroes: why zero-shot pruning is hard
Enzo Tartaglione · 2022
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Loss-based sensitivity regularization: towards deep sparse neural networks
Enzo Tartaglione, Andrea Bragagnolo, Attilio Fiandrotti, and Marco Grangetto · 2022
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Enzo Tartaglione, Stéphane Lathuilière, Attilio Fiandrotti, Marco Cagnazzo, and Marco Grangetto · 2021
Cited alongside, same era.
Recognition-aware deep video compression for remote surveillance
Florian Beye, Hayato Itsumi, Charvi Vitthal, and Koichi Nihei · 2022
Cited alongside, same era.
Anomaly detection in autonomous driving: A survey
Daniel Bogdoll, Maximilian Nitsche, and J Marius Zöllner · 2022
Cited alongside, same era.
Real-time defects detection for apple sorting using nir cameras with pruning-based yolov4 network
Shuxiang Fan, Xiaoting Liang, Wenqian Huang, Vincent Jialong Zhang, Qi Pang, Xin He, Lianjie Li, and Chi Zhang · 2022
Cited alongside, same era.
Later among the works it cites.
Transformer-based approach for document layout understanding
Huichen Yang and William Hsu · 2022
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Width & depth pruning for vision transformers
Fang Yu, Kun Huang, Meng Wang, Yuan Cheng, Wei Chu, and Li Cui · 2022
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Packed ensembles for efficient uncertainty estimation
Olivier Laurent, Adrien Lafage, Enzo Tartaglione, Geoffrey Daniel, Jean marc Martinez, Andrei Bursuc, and Gianni Franchi · 2023
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