Humans continually expand their learned knowledge to new domains and learn new concepts without any interference with past learned experiences. In contrast, machine learning models perform poorly in a continual learning setting, where input data distribution changes over time. Inspired by the nervous system learning mechanisms, we develop a computational model that enables a deep neural network to learn new concepts and expand its learned knowledge to new domains incrementally in a continual learning setting. We rely on the Parallel Distributed Processing theory to encode abstract concepts in an embedding space in terms of a multimodal distribution. This embedding space is modeled by internal data representations in a hidden network layer. We also leverage the Complementary Learning Systems theory to equip the model with a memory mechanism to overcome catastrophic forgetting through implementing pseudo-rehearsal. Our model can generate pseudo-data points for experience replay and accumulate new experiences to past learned experiences without causing cross-task interference.

翻译：人类不断扩展他们的学习知识到新的领域，并且在过去的学习经验没有任何干扰的情况下学习新概念。相比之下，机器学习模型在连续学习环境中表现不佳，其中输入数据分布随时间改变。受神经系统学习机制的启发，我们开发了一种计算模型，使深度神经网络能够在连续学习环境中增量地学习新概念和将其学习知识扩展到新领域。我们依靠并行分布处理理论，以多模态分布的形式在嵌入空间中编码抽象概念。嵌入空间由隐藏的网络层中的内部数据表示建模。我们还利用互补学习系统理论，通过实现假排练来为模型提供记忆机制，克服灾难性遗忘。我们的模型可以生成伪数据点进行经验重播，并将新经验积累到过去学习到的经验中，而不会引起跨任务干扰。