Optimizing Convolutional Neural Networks for Cloud
Detection
Author/Presenter
Event Type
Workshop
Deep Learning
Machine Learning
SIGHPC Workshop
TimeMonday, November 13th12pm -
12:30pm
Location502-503-504
DescriptionDeep convolutional neural networks (CNNs) have become
extremely popular and successful at a number of machine
learning tasks. One of the great challenges of
successfully deploying a CNN is designing the network:
specifying the network topology (sequence of layer
types) and configuring the network (setting all the
internal layer hyper-parameters). There are a number of
techniques which are commonly used to design the
network. One of the most successful is a simple (but
lengthy) random search.
In this paper, we demonstrate how a random search can be dramatically improved by a two-phase search. The first phase is a traditional random search on n network configurations. The second phase exploits a support vector machine to guide a second random search on N network configurations. We apply this technique to a dataset containing satellite imagery and demonstrate that we can, with very high accuracy, identify regions containing clouds which obscure the landscape below.
In this paper, we demonstrate how a random search can be dramatically improved by a two-phase search. The first phase is a traditional random search on n network configurations. The second phase exploits a support vector machine to guide a second random search on N network configurations. We apply this technique to a dataset containing satellite imagery and demonstrate that we can, with very high accuracy, identify regions containing clouds which obscure the landscape below.
Author/Presenter
