  Recently, it has been proposed that fruitful synergies may exist between Deep
Learning (DL) and Case Based Reasoning (CBR); that there are insights to be
gained by applying CBR ideas to problems in DL (what could be called DeepCBR).
In this paper, we report on a program of research that applies CBR solutions to
the problem of Explainable AI (XAI) in the DL. We describe a series of
twin-systems pairings of opaque DL models with transparent CBR models that
allow the latter to explain the former using factual, counterfactual and
semi-factual explanation strategies. This twinning shows that functional
abstractions of DL (e.g., feature weights, feature importance and decision
boundaries) can be used to drive these explanatory solutions. We also raise the
prospect that this research also applies to the problem of Data Augmentation in
DL, underscoring the fecundity of these DeepCBR ideas.
