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Multitask visual learning using genetic programming.

Wojciech Jaśkowski1, Krzysztof Krawiec, Bartosz Wieloch

  • 1Institute of Computing Science, Poznan University of Technology, Piotrowo 2, 60965 Poznań, Poland. wjaskowski@cs.put.poznan.pl

Evolutionary Computation
|December 5, 2008
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Summary
This summary is machine-generated.

This study introduces a multitask learning approach using genetic programming (GP) for visual concept learning. The method enhances shape recognition performance by enabling knowledge sharing between different visual tasks without increasing computational load.

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Machine Learning

Background:

  • Multitask learning aims to improve generalization by learning multiple related tasks simultaneously.
  • Genetic programming (GP) is an evolutionary computation technique for automatically generating computer programs.

Purpose of the Study:

  • To propose a novel multitask learning method for visual concept acquisition within the genetic programming framework.
  • To investigate the effectiveness of knowledge sharing between different visual tasks in GP.

Main Methods:

  • Developed a GP framework where individuals comprise multiple trees processing visual primitives.
  • Two trees are assigned to distinct visual tasks, sharing knowledge via common subfunction trees.
  • Task performance is evaluated based on the ability to reproduce shapes from training images.

Main Results:

  • The proposed multitask learning method was applied to shape recognition tasks.
  • Experimental results demonstrated performance improvements in one or both tasks compared to a reference method.
  • No additional computational effort was required for the observed performance gains.

Conclusions:

  • Multitask learning in GP can effectively improve visual concept learning.
  • Knowledge sharing between tasks within the GP framework leads to enhanced performance.
  • The method offers an efficient approach to multitask visual learning.