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A Committee of One - Using Dropout for Active Learning in Deep Networks

Gammelsæter, Martin
Master thesis
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URI
http://hdl.handle.net/11250/2352342
Date
2015
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  • Institutt for datateknologi og informatikk [3778]
Abstract
In many of the problem domains typically tackled by deep learning, data

is plentiful and cheap but labeling of the data is tedious and expensive.

Letting a model actively select the data instances it is uncertain about to train

on and ignore others can reduce the percentage of instances that must

be labeled to achieve satisfactory results. To this end, this project presents a

novel semi-supervised active learning algorithm called Active Deep Dropout

networks (ADD-networks). It is based on evaluating a deep neural network s

uncertainty on unlabeled instances, through measuring disagreement within

a committee of networks derived from the original network. The committee

members are Monte-Carlo-sampled from the full network using the concept

of dropout. Experiments on classifying handwritten digits show that ADD-

networks are comparable to a state-of-the-art method, and vastly outperforms

random selection of instances.
Publisher
NTNU

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