  TPC (Three-Phase Consolidation) is here introduced as a simple but effective
approach to continually learn new classes (and/or instances of known classes)
while controlling forgetting of previous knowledge. Each experience (a.k.a.
task) is learned in three phases characterized by different rules and learning
dynamics, aimed at removing the class-bias problem (due to class unbalancing)
and limiting gradient-based corrections to prevent forgetting of
underrepresented classes. Several experiments on complex datasets demonstrate
its accuracy and efficiency advantages over competitive existing approaches.
The algorithm and all the results presented in this paper are fully
reproducible thanks to its publication on the Avalanche open framework for
continual learning.
