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Exploring System Performance of Continual Learning for Mobile and Embedded Sensing Applications
[article]
2022
arXiv
pre-print
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
arXiv:2110.13290v2
fatcat:rtzneg33ijdw3hq4bhk6gtqk6y