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

Explainable Lifelong Stream Learning Based on "Glocal" Pairwise Fusion