Low-shot, Semi-supervised, Uncertainty Quantification Enabled Model for High Consequence HSI Data
Author:
Affiliation:
1. University of Arizona,Department of Computer Science,Tucson,AZ
2. Sandia National Laboratories,Albuquerque,NM
Funder
Sandia National Laboratories
National Nuclear Security Administration
U.S. Department of Energy
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/9843190/9843194/09843707.pdf?arnumber=9843707
Reference24 articles.
1. Neural Networks for Fingerprint Recognition
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4. Weight-averaged consistency targets improve semi-supervised deep learning results;tarvainen;CoRR,2017
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