Multi-resolution learning for knowledge transfer

Authors:
Eric Eaton
Affiliations:
University of Maryland Baltimore County, Department of Computer Science and Electrical Engineering, Baltimore, MD
Venue:
AAAI'06 proceedings of the 21st national conference on Artificial intelligence - Volume 2
Year:
2006

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Abstract

Related objects may look similar at low-resolutions; differences begin to emerge naturally as the resolution is increased. By learning across multiple resolutions of input, knowledge can be transfered between related objects. My dissertation develops this idea and applies it to the problem of multitask transfer learning.