论文标题
对人类国家监测的持续学习
Continual Learning for Human State Monitoring
论文作者
论文摘要
时间序列数据的持续学习（CL）代表了现实世界应用的有希望但知之甚少的途径。我们为人类国家监测提出了两个新的CLENG基准。我们仔细设计了基准，以反映现实世界中的环境，其中不断添加新主题。我们进行了经验评估，以评估流行策略减轻基准中忘记的能力。我们的结果表明，可能由于我们的基准的领域收入属性，即使使用简单的填充也可以很容易地解决忘记，并且现有的策略在积累了固定的，固定的，测试的主题方面累积知识。
Continual Learning (CL) on time series data represents a promising but under-studied avenue for real-world applications. We propose two new CL benchmarks for Human State Monitoring. We carefully designed the benchmarks to mirror real-world environments in which new subjects are continuously added. We conducted an empirical evaluation to assess the ability of popular CL strategies to mitigate forgetting in our benchmarks. Our results show that, possibly due to the domain-incremental properties of our benchmarks, forgetting can be easily tackled even with a simple finetuning and that existing strategies struggle in accumulating knowledge over a fixed, held-out, test subject.
