Thesis icon

Thesis

Learning and interpreting deep representations from multi-modal data

Abstract:

Deep learning has resulted in ground-breaking progress in a variety of domains, from core machine learning tasks such as image, language, and video understanding, to real-world industries such as medicine, autonomous driving, and agriculture. Its success has been driven by providing neural networks with manual supervision from large-scale labelled datasets such as ImageNet to automatically learn hierarchical data representations. However, obtaining large-scale labelled data is often a ve...

Expand abstract

Actions


Access Document


Files:

Authors


More by this author
Institution:
University of Oxford
Division:
MPLS
Department:
Engineering Science
Sub department:
Engineering Science
Research group:
Visual Geometry Group
Oxford college:
University College
Role:
Author

Contributors

Institution:
University of Oxford
Division:
MPLS
Department:
Engineering Science
Sub department:
Engineering Science
Research group:
Visual Geometry Group
Role:
Supervisor
ORCID:
0000-0003-1374-2858
Division:
MPLS
Department:
Engineering Science
Sub department:
Engineering Science
Research group:
Visual Geometry Group
Role:
Supervisor
Institution:
University of Oxford
Division:
MPLS
Department:
Engineering Science
Sub department:
Engineering Science
Research group:
Visual Geometry Group
Role:
Examiner
ORCID:
0000-0002-8945-8573
Institution:
University of Michigan
Role:
Examiner
More from this funder
Programme:
Rhodes Scholarship
Funding agency for:
Patrick, M
More from this funder
Programme:
Centre for Doctoral Training in Autonomous Intelligent Machines and Systems (AIMS)
Funding agency for:
Patrick, M
Grant:
EP/L015897/1
Type of award:
DPhil
Level of award:
Doctoral
Awarding institution:
University of Oxford

Terms of use


Views and Downloads






If you are the owner of this record, you can report an update to it here: Report update to this record

TO TOP