title: Multitask visual learning using genetic programming.
abstract: We propose a multitask learning method of visual concepts within the genetic programming (GP) framework. Each GP individual is composed of several trees that process visual primitives derived from input images. Two trees solve two different visual tasks and are allowed to share knowledge with each other by commonly calling the remaining GP trees (subfunctions) included in the same individual. The performance of a particular tree is measured by its ability to reproduce the shapes contained in the training images. We apply this method to visual learning tasks of recognizing simple shapes and compare it to a reference method. The experimental verification demonstrates that such multitask learning often leads to performance improvements in one or both solved tasks, without extra computational effort.
authors.affiliation: Institute of Computing Science, Poznan University of Technology, Piotrowo 2, 60965 Poznań, Poland. wjaskowski@cs.put.poznan.pl
authors.forename: Wojciech
authors.initials: W
authors.lastname: Jaśkowski
authors.forename: Krzysztof
authors.initials: K
authors.lastname: Krawiec
authors.forename: Bartosz
authors.initials: B
authors.lastname: Wieloch
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D019295:Computational Biology / Q000379:methods*; D004193:Discrimination Learning; D006801:Humans; D007858:Learning; D008957:Models, Genetic; D015233:Models, Statistical; D008962:Models, Theoretical; D010363:Pattern Recognition, Automated; D012016:Reference Values; D014796:Visual Perception