Abstract: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 sche… Show more
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