  Continual learning consists of algorithms that learn from a stream of
data/tasks continuously and adaptively thought time, enabling the incremental
development of ever more complex knowledge and skills. The lack of consensus in
evaluating continual learning algorithms and the almost exclusive focus on
forgetting motivate us to propose a more comprehensive set of implementation
independent metrics accounting for several factors we believe have practical
implications worth considering in the deployment of real AI systems that learn
continually: accuracy or performance over time, backward and forward knowledge
transfer, memory overhead as well as computational efficiency. Drawing
inspiration from the standard Multi-Attribute Value Theory (MAVT) we further
propose to fuse these metrics into a single score for ranking purposes and we
evaluate our proposal with five continual learning strategies on the iCIFAR-100
continual learning benchmark.
