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Exploring System Performance of Continual Learning for Mobile and Embedded Sensing Applications. (arXiv:2110.13290v2 [cs.LG] UPDATED)
cs.LG updates on arXiv.org arxiv.org
Continual learning approaches help deep neural network models adapt and learn
incrementally by trying to solve catastrophic forgetting. However, whether
these existing approaches, applied traditionally to image-based tasks, work
with the same efficacy to the sequential time series data generated by mobile
or embedded sensing systems remains an unanswered question.
To address this void, we conduct the first comprehensive empirical study that
quantifies the performance of three predominant continual learning schemes
(i.e., regularization, replay, and replay with examples) on six …
applications arxiv continual embedded learning lg mobile performance sensing