论文标题
连续学习，具有连续数据处理的封闭增量记忆
Continual Learning with Gated Incremental Memories for sequential data processing
论文作者
论文摘要
在动态的非组织环境中学习的能力而不忘记以前的知识（也称为持续学习（CL）），这是适应性解决方案的可扩展和可信赖的部署的关键推动力。尽管在机器视觉和强化学习问题中，持续学习的重要性在很大程度上得到了认可，但对于序列处理任务而言，这大多是记录的。这项工作提出了CL的经常性神经网络（RNN）模型，该模型能够处理输入分布中的概念漂移而不会忘记先前获得的知识。我们还在两种不同类型的RNN之上实施并测试流行的CL方法弹性重量合并（EWC）。最后，我们将增强体系结构与EWC和RNN的性能进行比较，并在一组标准的CLEN分析中进行了比较，适合顺序数据处理方案。结果表明，我们的体系结构的出色表现，并强调了旨在解决RNN中CL的特殊解决方案的需求。
The ability to learn in dynamic, nonstationary environments without forgetting previous knowledge, also known as Continual Learning (CL), is a key enabler for scalable and trustworthy deployments of adaptive solutions. While the importance of continual learning is largely acknowledged in machine vision and reinforcement learning problems, this is mostly under-documented for sequence processing tasks. This work proposes a Recurrent Neural Network (RNN) model for CL that is able to deal with concept drift in input distribution without forgetting previously acquired knowledge. We also implement and test a popular CL approach, Elastic Weight Consolidation (EWC), on top of two different types of RNNs. Finally, we compare the performances of our enhanced architecture against EWC and RNNs on a set of standard CL benchmarks, adapted to the sequential data processing scenario. Results show the superior performance of our architecture and highlight the need for special solutions designed to address CL in RNNs.
