In this study we examined the question of how error correction occurs in an ensemble of deep convolutional networks, trained for an important applied problem: segmentation of Electrocardiograms(ECG). We also explore the possibility of using the information about ensemble errors to evaluate a quality of data representation, built by the network. This possibility arises from the effect of distillation of outliers, which was demonstarted for the ensemble, described in this paper.
@article{arxiv.1812.10386,
title = {ECG Segmentation by Neural Networks: Errors and Correction},
author = {Iana Sereda and Sergey Alekseev and Aleksandra Koneva and Roman Kataev and Grigory Osipov},
journal= {arXiv preprint arXiv:1812.10386},
year = {2018}
}