  In the scenario of class-incremental learning (CIL), deep neural networks
have to adapt their model parameters to non-stationary data distributions,
e.g., the emergence of new classes over time. However, CIL models are
challenged by the well-known catastrophic forgetting phenomenon. Typical
methods such as rehearsal-based ones rely on storing exemplars of old classes
to mitigate catastrophic forgetting, which limits real-world applications
considering memory resources and privacy issues. In this paper, we propose a
novel rehearsal-free CIL approach that learns continually via the synergy
between two Complementary Learning Subnetworks. Our approach involves jointly
optimizing a plastic CNN feature extractor and an analytical feed-forward
classifier. The inaccessibility of historical data is tackled by holistically
controlling the parameters of a well-trained model, ensuring that the decision
boundary learned fits new classes while retaining recognition of previously
learned classes. Specifically, the trainable CNN feature extractor provides
task-dependent knowledge separately without interference; and the final
classifier integrates task-specific knowledge incrementally for decision-making
without forgetting. In each CIL session, it accommodates new tasks by attaching
a tiny set of declarative parameters to its backbone, in which only one matrix
per task or one vector per class is kept for knowledge retention. Extensive
experiments on a variety of task sequences show that our method achieves
competitive results against state-of-the-art methods, especially in accuracy
gain, memory cost, training efficiency, and task-order robustness. Furthermore,
to make the non-growing backbone (i.e., a model with limited network capacity)
suffice to train on more incoming tasks, a graceful forgetting implementation
on previously learned trivial tasks is empirically investigated.
