A New Framework for Semisupervised, Multitask Learning
Loeff, Nicolas
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https://hdl.handle.net/2142/81142
Description
Title
A New Framework for Semisupervised, Multitask Learning
Author(s)
Loeff, Nicolas
Issue Date
2009
Doctoral Committee Chair(s)
Ahuja, Narendra
Department of Study
Electrical and Computer Engineering
Discipline
Electrical and Computer Engineering
Degree Granting Institution
University of Illinois at Urbana-Champaign
Degree Name
Ph.D.
Degree Level
Dissertation
Keyword(s)
Engineering, Electronics and Electrical
Language
eng
Abstract
To conclude, we interpret the internal representation of the model and use it to perform unsupervised scene discovery. Defining a meaningful vocabulary for scene discovery is a challenging problem that has important consequences for object recognition. We consider scenes to depict correlated objects and present visual similarity. We postulate that the internal representation space of our model should allow us to discover a large number of scenes in unsupervised data; we show scene discrimination results on par with supervised approaches even without explicitly labeling scenes, producing highly plausible scene clusters.
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