New Metrics and Experimental Paradigms for Continual Learning

Tyler L. Hayes, Ronald Kemker, Nathan D. Cahill, Christopher Kanan; The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2018, pp. 2031-2034

Abstract


In order for a robotic agent to learn successfully in an uncontrolled environment, it must be able to immediately alter its behavior. Deep neural networks are the dominant approach for classification tasks in computer vision, but typical algorithms and architectures are incapable of immediately learning new tasks without catastrophically forgetting previously acquired knowledge. There has been renewed interest in solving this problem, but there are limitations to existing solutions, including poor performance compared to offline models, large memory footprints, and learning slowly. In this abstract, we formalize the continual learning paradigm and propose new benchmarks for assessing continual learning agents.

Related Material


[pdf]
[bibtex]
@InProceedings{Hayes_2018_CVPR_Workshops,
author = {Hayes, Tyler L. and Kemker, Ronald and Cahill, Nathan D. and Kanan, Christopher},
title = {New Metrics and Experimental Paradigms for Continual Learning},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops},
month = {June},
year = {2018}
}