  Deep learning models suffer from catastrophic forgetting when being
fine-tuned with samples of new classes. This issue becomes even more pronounced
when faced with the domain shift between training and testing data. In this
paper, we study the critical and less explored Domain-Generalized
Class-Incremental Learning (DGCIL). We design a DGCIL approach that remembers
old classes, adapts to new classes, and can classify reliably objects from
unseen domains. Specifically, our loss formulation maintains classification
boundaries and suppresses the domain-specific information of each class. With
no old exemplars stored, we use knowledge distillation and estimate old class
prototype drift as incremental training advances. Our prototype representations
are based on multivariate Normal distributions whose means and covariances are
constantly adapted to changing model features to represent old classes well by
adapting to the feature space drift. For old classes, we sample pseudo-features
from the adapted Normal distributions with the help of Cholesky decomposition.
In contrast to previous pseudo-feature sampling strategies that rely solely on
average mean prototypes, our method excels at capturing varying semantic
information. Experiments on several benchmarks validate our claims.
