Iterative Deep Neural Network Quantization With Lipschitz Constraint

Network quantization offers an effective solution to deep neural network compression for practical usage. Existing network quantization methods cannot theoretically guarantee the convergence. This paper proposes a novel iterative framework for network quantization with arbitrary bit-widths. We prese...

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Vydané v:	IEEE transactions on multimedia Ročník 22; číslo 7; s. 1874 - 1888
Hlavní autori:	Xu, Yuhui, Dai, Wenrui, Qi, Yingyong, Zou, Junni, Xiong, Hongkai
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Piscataway IEEE 01.07.2020 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:	Artificial neural networks Computational modeling Computer architecture Convergence Convolution Image coding iterative quantization Lipschitz constraint Measurement Network compression Neural networks Object detection Object recognition Partitions Performance enhancement Quantization (signal) Semantic segmentation Semantics State-of-the-art reviews
ISSN:	1520-9210, 1941-0077
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