In continual and lifelong learning, good representation learning can help increase performance and reduce sample complexity when learning new tasks. There is evidence that representations do not suffer from "catastrophic forgetting" even in plain continual learning, but little further fact is known about its characteristics. In this paper, we aim to gain more understanding about representation learning in continual learning, especially on the feature forgetting problem. We devise a protocol for evaluating representation in continual learning, and then use it to present an overview of the basic trends of continual representation learning, showing its consistent deficiency and potential issues. To study the feature forgetting problem, we create a synthetic dataset to identify and visualize the prevalence of feature forgetting in neural networks. Finally, we propose a simple technique using gating adapters to mitigate feature forgetting. We conclude by discussing that improving representation learning benefits both old and new tasks in continual learning.
翻译:在持续和终身学习中,良好的代表性学习有助于提高业绩,并在学习新任务时减少抽样复杂性。有证据表明,即使在普通的不断学习中,代表性的学习也不会受到“灾难性的遗忘”的影响,但对其特点的更多了解却很少。在本文件中,我们的目标是进一步了解在持续学习中的代表性学习,特别是特征的遗忘问题。我们设计了一个程序来评价持续学习中的代表性,然后用它来概述持续代表性学习的基本趋势,表明其一贯的缺陷和潜在的问题。为了研究特征,我们创建了一个合成数据集,以查明和直观地想象神经网络中特征的遗忘。最后,我们提出一种简单的技术,利用适应者来减轻特征的遗忘。我们的结论是,改进代表性学习有利于持续学习中的旧的和新的任务。