论文标题
人类活动识别中的持续学习：正则化的实证分析
Continual Learning in Human Activity Recognition: an Empirical Analysis of Regularization
论文作者
论文摘要
鉴于对重点关注计算机视觉领域的深层神经网络的持续学习技术的趋势不断增长，因此有必要确定哪些人可以很好地推广到其他任务，例如人类活动识别（HAR）。由于最近的方法主要由损失正规化术语和记忆重播组成，因此我们对在HAR数据集中采用这些任务的一些突出的任务收入学习技术进行了构成分析。我们发现，大多数正则化方法都缺乏实质性的效果，并提供了何时失败的直觉。因此，我们认为，不断学习算法的发展应由相当多的任务领域激励。
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.
