Less Labelled Learning
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2017-08-25
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en
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Abstract
For most of us humans, extracting information from visual data comes very
natural. For a computer however, recognising what it is looking at is a challenge.
To teach the computer to recognise the objects in an image, it needs to
be trained on a large dataset with many annotated images. Creating these annotations
is a time consuming and costly process, and the number of unlabelled
images available will always greatly outnumber the number of labelled images.
We propose a novel method for semi-supervised learning: Ordered ACOL-PL.
We show that our method is able to achieve a competitive classification accuracy
based on few samples in the training set accompanied by a class label. We
also explore the dependencies of the method on the selected and learned feature
space, as well as the dependency on the superclasses used.
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Faculteit der Sociale Wetenschappen