  In class-incremental learning, an agent with limited resources needs to learn
a sequence of classification tasks, forming an ever growing classification
problem, with the constraint of not being able to access data from previous
tasks. The main difference with task-incremental learning, where a task-ID is
available at inference time, is that the learner also needs to perform
cross-task discrimination, i.e. distinguish between classes that have not been
seen together. Approaches to tackle this problem are numerous and mostly make
use of an external memory (buffer) of non-negligible size. In this paper, we
ablate the learning of cross-task features and study its influence on the
performance of basic replay strategies used for class-IL. We also define a new
forgetting measure for class-incremental learning, and see that forgetting is
not the principal cause of low performance. Our experimental results show that
future algorithms for class-incremental learning should not only prevent
forgetting, but also aim to improve the quality of the cross-task features, and
the knowledge transfer between tasks. This is especially important when tasks
contain limited amount of data.
