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Thesis

Learning visual concepts with fewer human annotations

Abstract:

This thesis explores the use of modern deep neural networks to learn visual concepts with fewer human annotations on data. While data is abundant and increasingly easier to collect, most deep learning methods need extensive human labelling to be trained, which is often costly and may require expert-level knowledge. In this thesis we explore alternatives to human labelling by considering synthetic data, as well as partially and completely unlabelled data. We will study these alternatives wi...

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Division:
MPLS
Department:
Engineering Science
Role:
Author

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Role:
Supervisor
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Programme:
Integrated and Detailed Image Understanding
Grant:
IDIU-638009
Type of award:
DPhil
Level of award:
Doctoral
Awarding institution:
University of Oxford

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