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arXiv · 1704.06178

Exploring epoch-dependent stochastic residual networks

Fabio Carrara·Andrea Esuli·Fabrizio Falchi·Alejandro Moreo Fernández
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Abstract

The recently proposed stochastic residual networks selectively activate or bypass the layers during training, based on independent stochastic choices, each of which following a probability distribution that is fixed in advance. In this paper we present a first exploration on the use of an epoch-dependent distribution, starting with a higher probability of bypassing deeper layers and then activating them more frequently as training progresses. Preliminary results are mixed, yet they show some potential of adding an epoch-dependent management of distributions, worth of further investigation.

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Fabio Carrara, Andrea Esuli, Fabrizio Falchi, Alejandro Moreo Fernández. 2017-04-20. Exploring epoch-dependent stochastic residual networks. https://arxiv.org/abs/1704.06178

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