Artificial neural networks (ANNs) exhibit a narrow scope of expertise on stationary independent data. However, the data in the real world is continuous and dynamic, and ANNs must adapt to novel scenarios while also retaining the learned knowledge to become lifelong learners. The ability of humans to excel at these tasks can be attributed to multiple factors ranging from cognitive computational structures, cognitive biases, and the multi-memory systems in the brain. We incorporate key concepts from each of these to design a novel framework, Dual Cognitive Architecture (DUCA), which includes multiple sub-systems, implicit and explicit knowledge representation dichotomy, inductive bias, and a multi-memory system. The inductive bias learner within DUCA is instrumental in encoding shape information, effectively countering the tendency of ANNs to learn local textures. Simultaneously, the inclusion of a semantic memory submodule facilitates the gradual consolidation of knowledge, replicating the dynamics observed in fast and slow learning systems, reminiscent of the principles underpinning the complementary learning system in human cognition. DUCA shows improvement across different settings and datasets, and it also exhibits reduced task recency bias, without the need for extra information. To further test the versatility of lifelong learning methods on a challenging distribution shift, we introduce a novel domain-incremental dataset DN4IL. In addition to improving performance on existing benchmarks, DUCA also demonstrates superior performance on this complex dataset.

翻译：人工神经网络（ANNs）在静态独立数据上表现出狭窄的专业化能力。然而，现实世界的数据具有连续性和动态性，ANNs必须适应新场景，同时保留已学知识以实现终身学习。人类在这些任务上的卓越能力可归因于认知计算结构、认知偏差及大脑多记忆系统等多个因素。我们整合这些关键概念，设计了一种新型框架——双认知架构（DUCA），其包含多个子系统、隐式与显式知识表征二分法、归纳偏差以及多记忆系统。DUCA中的归纳偏差学习器在编码形状信息方面发挥关键作用，有效对抗ANNs倾向于学习局部纹理的固有缺陷。同时，语义记忆子模块的加入促进了知识的渐进巩固，复现了快慢学习系统中的动态过程，这类似于人类认知互补学习系统的基本原理。DUCA在不同设置和数据集上均展现出性能提升，且在不需额外信息的情况下降低了任务新近偏差。为检验终身学习方法在更具挑战性的分布偏移下的通用性，我们引入新型域增量数据集DN4IL。除在现有基准上提升性能外，DUCA在此复杂数据集上亦表现出卓越性能。