Channel-Wise Bit Allocation for Deep Visual Feature Quantization
Author:
Affiliation:
1. Chinese Academy of Sciences,State Key Laboratory of Space Weather, National Space Science Center,Beijing,China,100190
2. Institute for Infocomm Research,A*STAR,Singapore
3. Nanyang Technological University,Singapore
Funder
Peng Cheng Laboratory
National Natural Science Foundation of China
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/9897158/9897159/09897325.pdf?arnumber=9897325
Reference24 articles.
1. Dorefa-net: Training low bitwidth convolutional neural networks with low bitwidth gradients;zhou,2016
2. Deep residual learning for image recognition;he;Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition,2016
3. Towards Effective Low-Bitwidth Convolutional Neural Networks
4. Very deep convolutional networks for large-scale image recognition;simonyan,2014
5. Releq: an automatic reinforcement learning approach for deep quantization of neural networks;elthakeb;NeurIPS ML for Systems workshop 2018,2019
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