Humans excel at continually learning from an ever-changing environment whereas it remains a challenge for deep neural networks which exhibit catastrophic forgetting. The complementary learning system (CLS) theory suggests that the interplay between rapid instance-based learning and slow structured learning in the brain is crucial for accumulating and retaining knowledge. Here, we propose CLS-ER, a novel dual memory experience replay (ER) method which maintains short-term and long-term semantic memories that interact with the episodic memory. Our method employs an effective replay mechanism whereby new knowledge is acquired while aligning the decision boundaries with the semantic memories. CLS-ER does not utilize the task boundaries or make any assumption about the distribution of the data which makes it versatile and suited for "general continual learning". Our approach achieves state-of-the-art performance on standard benchmarks as well as more realistic general continual learning settings.

翻译：人类在不断从不断变化的环境中不断学习,而对于出现灾难性遗忘的深层神经网络来说,这仍然是一项挑战。补充学习系统(CLS)的理论表明,快速实例学习和大脑中缓慢结构化学习之间的相互作用对于积累和保存知识至关重要。在这里,我们提出CLS-ER,这是一个新的双记忆回放(ER)方法,它保持短期和长期的语义记忆,与偶发记忆互动。我们的方法使用一种有效的回放机制,在获取新知识的同时,将决定的界限与语义记忆统一起来。 CLS-ER不使用任务界限,也不对如何分配数据进行任何假设,使其具有多功能并适合“一般持续学习”。我们的方法在标准基准上实现了最先进的表现,并在更现实的一般持续学习环境中实现了更现实的不断学习环境。