CQ$^{+}$ Training: Minimizing Accuracy Loss in Conversion from Convolutional Neural Networks to Spiking Neural Networks
Author:
Affiliation:
1. School of Computing, National University of Singapore, Singapore
2. Institute of High Performance Computing (IHPC), Agency for Science Technology and Research (A*STAR)
Funder
programmatic
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
Applied Mathematics,Artificial Intelligence,Computational Theory and Mathematics,Computer Vision and Pattern Recognition,Software
Link
http://xplorestaging.ieee.org/ielx7/34/4359286/10152465.pdf?arnumber=10152465
Reference42 articles.
1. Philosophy of the Spike: Rate-Based vs. Spike-Based Theories of the Brain
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3. Neuronal variability: noise or part of the signal?
4. Slayer: Spike layer error reassignment in time;shrestha;Proc Adv Neural Inf Process Syst,2018
5. Batch normalization: Accelerating deep network training by reducing internal covariate shift;ioffe,2015
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