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.

翻译：近期研究表明，通过使用大型预训练模型与可学习提示相结合，类增量学习（CIL）场景下的无重放方法能够取得优于主流重放方法的性能。无重放CIL方法在区分不同任务中的类别时存在困难，因为这些类别未经过联合训练。本文提出一种基于虚拟异常的正则化方法，通过收紧分类器的决策边界来缓解跨任务类别的混淆问题。现行基于提示的方法通常需要维护一个任务专用提示池，以防止新任务知识覆盖先前任务知识，这导致查询和组合最优提示的额外计算开销。本文揭示，在不牺牲准确率的前提下，这类额外成本完全可以消除。我们证明，采用简化提示方法即可获得与配备提示池的最先进方法（SOTA）相当的性能，同时显著减少可学习参数和推理成本。所提出的正则化方法展现出与多种基于提示方法兼容的特性，在ImageNet-R和CIFAR-100基准测试中，有效提升了此前SOTA无重放CIL方法的准确率。我们的源代码已发布于https://github.com/jpmorganchase/ovor。