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.

翻译：实时设备上的持续学习应用广泛应用于手机、消费机器人和智能家电。这类设备处理能力和内存存储有限，而持续学习需要长期采集数据。因此，终身学习算法必须能在这些约束下运行，同时保持良好的性能。本研究提出了可解释终身学习（ExLL）模型，该模型具备以下重要特性：1）能够在单次处理中从样本和资源匮乏的流数据中“学会学习”；2）基于自组织原型的架构，按需扩展，通过相似性将流数据聚类为可分离组，并保留数据以防止灾难性遗忘；3）可解释架构，将聚类结果转化为可解释的IF-THEN规则，并根据推理结果的相似性与差异性解释模型预测；4）通过成对决策融合过程在全局和局部层面进行推理，以提高推理准确性，即“全局-局部成对融合”。我们使用OpenLoris、F-SIOL-310和Places数据集，将ExLL与当代在线学习算法在图像识别任务中进行比较，评估了视频流、小样本学习、可扩展性及数据流不平衡等多种持续学习场景。算法在准确性、参数数量及实验运行时间方面进行了性能评估。在大多数测试场景中，ExLL在准确性上优于所有算法。