Is Active Learning Always Beneficial? (Student Abstract)
Keywords:Active Learning, Curriculum Learning, Curiosity-driven Learning, Deep Learning, Category Recognition
AbstractThis 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.
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
AAAI Student Abstract and Poster Program