DenseShift : Towards Accurate and Efficient Low-Bit Power-of-Two Quantization-Reference-Cited by-同舟云学术

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DenseShift : Towards Accurate and Efficient Low-Bit Power-of-Two Quantization

Published:2023-10-01 Issue: Volume:34 Page:16964-16974
ISSN:
Container-title:2023 IEEE/CVF International Conference on Computer Vision (ICCV)
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Author:

Li Xinlin¹,Liu Bang²,Yang Rui Heng¹,Courville Vanessa¹,Xing Chao¹,Nia Vahid Partovi¹

Affiliation:

1. Huawei Technologies,Noah’s Ark Lab

2. University of Montreal & Mila - Quebec AI Institute

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10376473/10376477/10378306.pdf?arnumber=10378306

Reference51 articles.

1. An empirical study of binary neural networks’ optimisation;Alizadeh

2. Low-bit Shift Network for End-to-End Spoken Language Understanding

3. Deep Learning with Low Precision by Half-Wave Gaussian Quantization

4. AdderNet: Do We Really Need Multiplications in Deep Learning?

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