Curriculum learning of multiple tasks Conference Paper

Author(s): Pentina, Anastasia; Sharmanska, Viktoriia; Lampert, Christoph
Title: Curriculum learning of multiple tasks
Affiliation IST Austria
Abstract: Sharing information between multiple tasks enables algorithms to achieve good generalization performance even from small amounts of training data. However, in a realistic scenario of multi-task learning not all tasks are equally related to each other, hence it could be advantageous to transfer information only between the most related tasks. In this work we propose an approach that processes multiple tasks in a sequence with sharing between subsequent tasks instead of solving all tasks jointly. Subsequently, we address the question of curriculum learning of tasks, i.e. finding the best order of tasks to be learned. Our approach is based on a generalization bound criterion for choosing the task order that optimizes the average expected classification performance over all tasks. Our experimental results show that learning multiple related tasks sequentially can be more effective than learning them jointly, the order in which tasks are being solved affects the overall performance, and that our model is able to automatically discover the favourable order of tasks.
Conference Title: CVPR: Computer Vision and Pattern Recognition
Conference Dates: June 7-12, 2015
Conference Location: Boston, MA, USA
Publisher: IEEE  
Date Published: 2015-06-01
Start Page: 5492
End Page: 5500
DOI: 10.1109/CVPR.2015.7299188
Open access: yes (repository)
IST Austria Authors
  1. Christoph Lampert
    87 Lampert
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