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Thesis

Deep learning with synthetic, temporal, and adversarial supervision

Abstract:

In this thesis we explore alternatives to manually annotated training examples for supervising the training of deep learning models. Specifically, we develop methods for learning under three different supervision paradigms, namely — (1) synthetic data, (2) temporal data, and (3) adversarial supervision for learning from unaligned examples. The dominant application domain of our work is text spotting, i.e. detection and recognition of text instances in images. We learn text localisation net...

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Department:
Engineering Science

Contributors

Department:
Engineering Science, University of Oxford
Role:
Supervisor
Department:
Engineering Science, University of Oxford
Role:
Supervisor
Type of award:
DPhil
Level of award:
Doctoral
Awarding institution:
University of Oxford

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