We present a variational approximation to the information bottleneck of
Tishby et al. (1999). This variational approach allows us to parameterize the
information bottleneck model using a neural network and leverage the
reparameterization trick for efficient training. We call this method "Deep
Variational Information Bottleneck", or Deep VIB. We show that models trained
with the VIB objective outperform those that are trained with other forms of
regularization, in terms of generalization performance and robustness to
adversarial attack.
Description
[1612.00410] Deep Variational Information Bottleneck
%0 Generic
%1 alemi2016variational
%A Alemi, Alexander A.
%A Fischer, Ian
%A Dillon, Joshua V.
%A Murphy, Kevin
%D 2016
%K SHOULDREAD information-bottleneck machine-learning
%T Deep Variational Information Bottleneck
%U http://arxiv.org/abs/1612.00410
%X We present a variational approximation to the information bottleneck of
Tishby et al. (1999). This variational approach allows us to parameterize the
information bottleneck model using a neural network and leverage the
reparameterization trick for efficient training. We call this method "Deep
Variational Information Bottleneck", or Deep VIB. We show that models trained
with the VIB objective outperform those that are trained with other forms of
regularization, in terms of generalization performance and robustness to
adversarial attack.
@misc{alemi2016variational,
abstract = {We present a variational approximation to the information bottleneck of
Tishby et al. (1999). This variational approach allows us to parameterize the
information bottleneck model using a neural network and leverage the
reparameterization trick for efficient training. We call this method "Deep
Variational Information Bottleneck", or Deep VIB. We show that models trained
with the VIB objective outperform those that are trained with other forms of
regularization, in terms of generalization performance and robustness to
adversarial attack.},
added-at = {2017-10-08T20:45:52.000+0200},
author = {Alemi, Alexander A. and Fischer, Ian and Dillon, Joshua V. and Murphy, Kevin},
biburl = {https://www.bibsonomy.org/bibtex/26f2293a54cedf90514a456d1731cb8d1/marcsaric},
description = {[1612.00410] Deep Variational Information Bottleneck},
interhash = {d09bedae8064951e1c28ea8b29a3ce9c},
intrahash = {6f2293a54cedf90514a456d1731cb8d1},
keywords = {SHOULDREAD information-bottleneck machine-learning},
note = {cite arxiv:1612.00410Comment: 19 pages, 8 figures, Accepted to ICLR17},
timestamp = {2017-10-08T20:45:52.000+0200},
title = {Deep Variational Information Bottleneck},
url = {http://arxiv.org/abs/1612.00410},
year = 2016
}