论文标题
种群：生理信号的持续学习
CLOPS: Continual Learning of Physiological Signals
论文作者
论文摘要
当实例违反独立和相同分布的假设时，深度学习算法会经历破坏性干扰（i.i.d）。但是，这种违规在临床环境中无处不在，在临床环境中，数据是从暂时流出的，并且是从多种生理传感器流中流传输的。为了克服这一障碍，我们提出了Clops，这是一种基于重播的持续学习策略。在基于三个公开可用数据集的三个持续学习场景中，我们表明clops可以胜过最先进的方法，即宝石和mir。此外，我们提出了端到端可训练的参数，我们将其称为“任务 - 内置参数”，可用于量化任务难度和相似性。该量化可以洞悉网络可解释性和临床应用，而任务困难的量化很差。
Deep learning algorithms are known to experience destructive interference when instances violate the assumption of being independent and identically distributed (i.i.d). This violation, however, is ubiquitous in clinical settings where data are streamed temporally and from a multitude of physiological sensors. To overcome this obstacle, we propose CLOPS, a replay-based continual learning strategy. In three continual learning scenarios based on three publically-available datasets, we show that CLOPS can outperform the state-of-the-art methods, GEM and MIR. Moreover, we propose end-to-end trainable parameters, which we term task-instance parameters, that can be used to quantify task difficulty and similarity. This quantification yields insights into both network interpretability and clinical applications, where task difficulty is poorly quantified.
