Optimizing Convolutional Neural Networks for Fast Training on a Small Dataset
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2016-05-17
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en
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Abstract
Since a few years convolutional neural networks are
state of the art for image recognition tasks. However
training these networks is time and computationally
intensive and there are a lot of optimizable parameters
involved. There is a lot of literature describing
the best network structures and parameters to get
the best results on well known and large datasets
after hours of training time. In contrast much less
is known on how to get fast and good results on a
small dataset. In this paper we present approaches
to quickly train good performing convolutional neural
networks on a small dataset so we can solve a
problem with brain data (for which the amount of
available data is limited in most cases). We found
that network structure related parameters together
with learning rate (and its decay) are the best choice
of parameters to optimize your network with.
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Faculteit der Sociale Wetenschappen