Reference health
Filter-based deep-compression with global average pooling for convolutional networks
5/5 checkable references clean · checked 2026-08-11
Every reference with a DOI in the deposited reference list resolved to a known work in Crossref or DataCite at the dated check, and none carried a retraction, withdrawal, or removal notice.
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The 5 checked references that resolve
resolves10.14257/astl.2016.140.36
Hierarchical Compression of Deep Convolutional Neural Networks on Large Scale Visual Recognition for Mobile Applications
resolves10.1609/aaai.v32i1.11663
Deep Neural Network Compression With Single and Multiple Level Quantization
resolves10.1109/ICCV.2017.541
ThiNet: A Filter Level Pruning Method for Deep Neural Network Compression
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no DOI — not checkedExploiting linear structure within convolutional networks for efficient evaluation
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no DOI — not checkedJ. Guo, Y. Li, W. Lin, Y. Chen, J. Li, Network decoupling: from regular to depthwise separable convolutions, arXiv:1808.05517 (2018).
no DOI — not checkedM. Lin, Q. Chen, S. Yan, Network in network, arXiv:1312.4400 (2013).
no DOI — not checkedRestructuring of deep neural network acoustic models with singular value decomposition.
no DOI — not checkedVlfeat: An open and portable library of computer vision algorithms
no DOI — not checkedCaffe: Convolutional architecture for fast feature embedding
no DOI — not checkedK. Simonyan, A. Zisserman, Very deep convolutional networks for large-scale image recognition, arXiv:1409.1556 (2014).
no DOI — not checkedH. Hu, R. Peng, Y.-W. Tai, C.-K. Tang, Network trimming: a data-driven neuron pruning approach towards efficient deep architectures, arXiv:1607.03250 (2016).
no DOI — not checkedEfficient and accurate approximations of nonlinear convolutional networks
no DOI — not checkedOn compressing deep models by low rank and sparse decomposition
no DOI — not checkedDeep compression: compressing deep neural network with pruning, trained quantization and huffman coding
no DOI — not checkedY. He, X. Zhang, J. Sun, Channel pruning for accelerating very deep neural networks, arXiv:1707.06168.
no DOI — not checked10.1016/j.sysarc.2019.02.008_bib0025
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