  Continual learning (CL) has spurred the development of several methods aimed
at consolidating previous knowledge across sequential learning. Yet, the
evaluations of these methods have primarily focused on the final output, such
as changes in the accuracy of predicted classes, overlooking the issue of
representational forgetting within the model. In this paper, we propose a novel
representation-based evaluation framework for CL models. This approach involves
gathering internal representations from throughout the continual learning
process and formulating three-dimensional tensors. The tensors are formed by
stacking representations, such as layer activations, generated from several
inputs and model `snapshots', throughout the learning process. By conducting
tensor component analysis (TCA), we aim to uncover meaningful patterns about
how the internal representations evolve, expecting to highlight the merits or
shortcomings of examined CL strategies. We conduct our analyses across
different model architectures and importance-based continual learning
strategies, with a curated task selection. While the results of our approach
mirror the difference in performance of various CL strategies, we found that
our methodology did not directly highlight specialized clusters of neurons, nor
provide an immediate understanding the evolution of filters. We believe a
scaled down version of our approach will provide insight into the benefits and
pitfalls of using TCA to study continual learning dynamics.
