A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf
.
Deep Neural Network Ensembles against Deception: Ensemble Diversity, Accuracy and Robustness
[article]
2019
arXiv
pre-print
Ensemble learning is a methodology that integrates multiple DNN learners for improving prediction performance of individual learners. Diversity is greater when the errors of the ensemble prediction is more uniformly distributed. Greater diversity is highly correlated with the increase in ensemble accuracy. Another attractive property of diversity optimized ensemble learning is its robustness against deception: an adversarial perturbation attack can mislead one DNN model to misclassify but may
arXiv:1908.11091v1
fatcat:oo7wgjsupbhdno577qirsosbr4