Learning by Watching and Learning by Doing

When we are babies, we learn how to see by watching how the world changes and by interacting with it. Can we use these same signals to train vision models? In this thesis, we outline several works which use these paradigms as a basis for learning algorithms. First, we explore learning by watching in which video data is directly used to learn about the visual world. Second, we tackle multiple challenging tasks in embodied environments in which agents learn by interacting with their surroundings.

Author dc:creator

• Gordon, Daniel

Advisors dc:contributor.advisor

• Farhadi, Ali
• Fox, Dieter

• Computer Vision
• Deep Learning
• Machine Learning
• Computer science
• Robotics

Statement dc:rights

• CC BY-SA

Language dc:language.iso

en_US

Handle dc:identifier.uri

http://hdl.handle.net/1773/45931

OAI identifier oai:identifier

oai:digital.lib.washington.edu:1773/45931