论文标题
终身流浪：一个现实的在线持续学习环境
Lifelong Wandering: A realistic few-shot online continual learning setting
论文作者
论文摘要
在线少数学习描述了一个设置，在学习新兴课程时，在数据流中对模型进行了培训和评估。尽管此环境中的先前工作在实例分类中从一个由单个室内环境组成的数据流中学习实现了非常有前途的性能，但我们建议扩展此设置，以考虑在一系列室内环境中考虑对象分类，这可能会发生在诸如Robotics之类的应用中。重要的是，我们称之为在线持续学习的环境将灾难性遗忘的灾难性遗忘的问题注入了少数射击的在线学习范式中。在这项工作中，我们在我们的环境中基准了几种现有的方法和改编的基线，并证明灾难性遗忘和在线绩效之间存在权衡。我们的发现激发了这种环境中未来工作的需求，这可以在不灾难性遗忘的情况下实现更好的在线表现。
Online few-shot learning describes a setting where models are trained and evaluated on a stream of data while learning emerging classes. While prior work in this setting has achieved very promising performance on instance classification when learning from data-streams composed of a single indoor environment, we propose to extend this setting to consider object classification on a series of several indoor environments, which is likely to occur in applications such as robotics. Importantly, our setting, which we refer to as online few-shot continual learning, injects the well-studied issue of catastrophic forgetting into the few-shot online learning paradigm. In this work, we benchmark several existing methods and adapted baselines within our setting, and show there exists a trade-off between catastrophic forgetting and online performance. Our findings motivate the need for future work in this setting, which can achieve better online performance without catastrophic forgetting.
