  Recent works have shown that by using large pre-trained models along with
learnable prompts, rehearsal-free methods for class-incremental learning (CIL)
settings can achieve superior performance to prominent rehearsal-based ones.
Rehearsal-free CIL methods struggle with distinguishing classes from different
tasks, as those are not trained together. In this work we propose a
regularization method based on virtual outliers to tighten decision boundaries
of the classifier, such that confusion of classes among different tasks is
mitigated. Recent prompt-based methods often require a pool of task-specific
prompts, in order to prevent overwriting knowledge of previous tasks with that
of the new task, leading to extra computation in querying and composing an
appropriate prompt from the pool. This additional cost can be eliminated,
without sacrificing accuracy, as we reveal in the paper. We illustrate that a
simplified prompt-based method can achieve results comparable to previous
state-of-the-art (SOTA) methods equipped with a prompt pool, using much less
learnable parameters and lower inference cost. Our regularization method has
demonstrated its compatibility with different prompt-based methods, boosting
those previous SOTA rehearsal-free CIL methods' accuracy on the ImageNet-R and
CIFAR-100 benchmarks. Our source code is available at
https://github.com/jpmorganchase/ovor.
