Continual learning in human activity recognition : an empirical analysis of regularization
Abstract
Given the growing trend of continual learning techniques for deep neural networks focusing on the domain of computer vision, there is a need to identify which of these generalizes well to other tasks such as human activity recognition (HAR). As recent methods have mostly been composed of loss regularization terms and memory replay, we provide a constituent-wise analysis of some prominent task-incremental learning techniques employing these on HAR datasets. We find that most regularization approaches lack substantial effect and provide an intuition of when they fail. Thus, we make the case that the development of continual learning algorithms should be motivated by rather diverse task domains.
Citation
Jha , S , Schiemer , M & Ye , J 2020 , ' Continual learning in human activity recognition : an empirical analysis of regularization ' , Paper presented at Thirty-seventh International Conference on Machine Learning, ICML 2020; ICML Workshop on Continual Learning , 13/07/20 - 18/07/20 pp. 1-4 . workshop
Publication
Status
Peer reviewed
Type
Conference paper
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Copyright © 2020 the Author(s). This work has been made available online in accordance with publisher policies or with permission. Permission for further reuse of this content should be sought from the publisher or the rights holder. This is the author created accepted manuscript following peer review and may differ slightly from the final published version. The final published version of this work is available at https://sites.google.com/view/cl-icml/accepted-papers
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