Thesis
Deep learning with synthetic, temporal, and adversarial supervision
- Abstract:
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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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Authors
Contributors
+ Vedaldi, A
Institution:
University of Oxford
Division:
MPLS
Department:
Engineering Science
Role:
Supervisor
+ Zisserman, A
Institution:
University of Oxford
Division:
MPLS
Department:
Engineering Science
Role:
Supervisor
Funding
+ UK EPSRC CDT in Autonomous Intelligent Machines and Systems
More from this funder
Grant:
EP/L015987/2
Bibliographic Details
- Type of award:
- DPhil
- Level of award:
- Doctoral
- Awarding institution:
- University of Oxford
Item Description
- UUID:
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uuid:0a9d2c4a-0e8d-4722-9e0b-3f247efa89e8
- Deposit date:
- 2019-04-14
Related Items
Terms of use
- Copyright holder:
- Gupta, A
- Copyright date:
- 2018
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