Is Active Learning Always Beneficial? (Student Abstract)

Authors

  • Anna Kravchenko Trinity College Dublin
  • Rhodri Cusack Trinity College Dublin

Keywords:

Active Learning, Curriculum Learning, Curiosity-driven Learning, Deep Learning, Category Recognition

Abstract

This study highlights the limitations of automated curriculum learning, which may not be a viable strategy for tasks in which the benefits of the chosen curriculum are not apparent until much later. Using a simple convolutional network and a two-task training regime, we show that in some cases a network is not able to derive an optimal learning strategy using only the data available during a single training run.

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Published

2021-05-18

How to Cite

Kravchenko, A., & Cusack, R. (2021). Is Active Learning Always Beneficial? (Student Abstract). Proceedings of the AAAI Conference on Artificial Intelligence, 35(18), 15819-15820. Retrieved from https://ojs.aaai.org/index.php/AAAI/article/view/17906

Issue

Section

AAAI Student Abstract and Poster Program