Taming the ReLU with Parallel Dither in a Deep Neural Network
Machine Learning
2015-09-18 v1
Abstract
Rectified Linear Units (ReLU) seem to have displaced traditional 'smooth' nonlinearities as activation-function-du-jour in many - but not all - deep neural network (DNN) applications. However, nobody seems to know why. In this article, we argue that ReLU are useful because they are ideal demodulators - this helps them perform fast abstract learning. However, this fast learning comes at the expense of serious nonlinear distortion products - decoy features. We show that Parallel Dither acts to suppress the decoy features, preventing overfitting and leaving the true features cleanly demodulated for rapid, reliable learning.
Keywords
Cite
@article{arxiv.1509.05173,
title = {Taming the ReLU with Parallel Dither in a Deep Neural Network},
author = {Andrew J. R. Simpson},
journal= {arXiv preprint arXiv:1509.05173},
year = {2015}
}