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What is the state of the art in continual machine learning? Although anatural question for predominant static benchmarks, the notion to train systemsin a lifelong manner entails a plethora of additional challenges with respectto set-up and evaluation. The latter have recently sparked a growing amount ofcritiques on prominent algorithm-centric perspectives and evaluation protocolsbeing too narrow, resulting in several attempts at constructing guidelines infavor of specific desiderata or arguing against the validity of prevalentassumptions. In this work, we depart from this mindset and argue that the goalof a precise formulation of desiderata is an ill-posed one, as diverseapplications may always warrant distinct scenarios. Instead, we introduce theContinual Learning EValuation Assessment Compass: the CLEVA-Compass. Thecompass provides the visual means to both identify how approaches arepractically reported and how works can simultaneously be contextualized in thebroader literature landscape. In addition to promoting compact specification inthe spirit of recent replication trends, it thus provides an intuitive chart tounderstand the priorities of individual systems, where they resemble eachother, and what elements are missing towards a fair comparison.

CLEVA-Compass: A Continual Learning EValuation Assessment Compass to Promote Research Transparency and Comparability