Less Labelled Learning

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Issue Date
2017-08-25
Language
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