Real-time on-device continual learning applications are used on mobile phones, consumer robots, and smart appliances. Such devices have limited processing and memory storage capabilities, whereas continual learning acquires data over a long period of time. By necessity, lifelong learning algorithms have to be able to operate under such constraints while delivering good performance. This study presents the Explainable Lifelong Learning (ExLL) model, which incorporates several important traits: 1) learning to learn, in a single pass, from streaming data with scarce examples and resources; 2) a self-organizing prototype-based architecture that expands as needed and clusters streaming data into separable groups by similarity and preserves data against catastrophic forgetting; 3) an interpretable architecture to convert the clusters into explainable IF-THEN rules as well as to justify model predictions in terms of what is similar and dissimilar to the inference; and 4) inferences at the global and local level using a pairwise decision fusion process to enhance the accuracy of the inference, hence ``Glocal Pairwise Fusion.'' We compare ExLL against contemporary online learning algorithms for image recognition, using OpenLoris, F-SIOL-310, and Places datasets to evaluate several continual learning scenarios for video streams, low-sample learning, ability to scale, and imbalanced data streams. The algorithms are evaluated for their performance in accuracy, number of parameters, and experiment runtime requirements. ExLL outperforms all algorithms for accuracy in the majority of the tested scenarios.

该研究提出了一个Explainable Lifelong Learning (ExLL)模型，适用于移动电话、消费机器人和智能家电等实时的、在设备上的连续学习应用中，通过一个自组织的基于原型的架构将流式数据聚类为可分离的group，并保留数据，同时提供IF-THEN规则、模型预测的解释以及全局和本地决策融合等诸多性能优点，相对于其他在线学习算法在视频流、低样本学习、规模能力和不平衡数据流等多种场景下的准确性表现更优。

基于“全球-局部”配对融合的可解释的生命周期流学习