Explainable Lifelong Stream Learning Based on "Glocal" Pairwise Fusion
Chu Kiong Loo Wei Shiung Liew Stefan Wermter
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.