In the context of continual learning, prototypes-as representative class embeddings-offer advantages in memory conservation and the mitigation of catastrophic forgetting. However, challenges related to semantic drift and prototype interference persist. In this study, we introduce the Contrastive Prototypical Prompt (CPP) approach. Through task-specific prompt-tuning, underpinned by a contrastive learning objective, we effectively address both aforementioned challenges. Our evaluations on four challenging class-incremental benchmarks reveal that CPP achieves a significant 4% to 6% improvement over state-of-the-art methods. Importantly, CPP operates without a rehearsal buffer and narrows the performance divergence between continual and offline joint-learning, suggesting an innovative scheme for Transformer-based continual learning systems.

翻译：在持续学习背景下，原型（作为类别代表性嵌入）在内存节约与缓解灾难性遗忘方面具有优势，但语义偏移与原型干扰问题仍然存在。本研究提出对比原型提示（CPP）方法，通过基于对比学习目标的任务特定提示调优，有效解决了上述两大挑战。我们在四个具有挑战性的类别增量基准测试上的评估显示，CPP相较于现有最优方法实现了4%至6%的显著提升。尤为重要的是，CPP无需回放缓冲区即可运作，并缩小了持续学习与离线联合学习之间的性能差距，为基于Transformer的持续学习系统提供了创新范式。