Prototype, as a representation of class embeddings, has been explored to reduce memory footprint or mitigate forgetting for continual learning scenarios. However, prototype-based methods still suffer from abrupt performance deterioration due to semantic drift and prototype interference. In this study, we propose Contrastive Prototypical Prompt (CPP) and show that task-specific prompt-tuning, when optimized over a contrastive learning objective, can effectively address both obstacles and significantly improve the potency of prototypes. Our experiments demonstrate that CPP excels in four challenging class-incremental learning benchmarks, resulting in 4% to 6% absolute improvements over state-of-the-art methods. Moreover, CPP does not require a rehearsal buffer and it largely bridges the performance gap between continual learning and offline joint-learning, showcasing a promising design scheme for continual learning systems under a Transformer architecture.

翻译：原型作为类别嵌入的表征，已被探索用于减少连续学习场景中的内存占用或缓解遗忘问题。然而，基于原型的方法仍因语义漂移和原型干扰而面临性能急剧退化。在本研究中，我们提出对比原型提示（CPP），并证明当通过对比学习目标进行优化时，任务特定的提示微调能有效解决这两大障碍，显著提升原型的效能。实验表明，CPP在四个具有挑战性的类增量学习基准上表现优异，相较于最新方法实现4%至6%的绝对性能提升。此外，CPP无需重放缓冲区，且大幅缩小了连续学习与离线联合学习之间的性能差距，展示了基于Transformer架构的连续学习系统的一种极具前景的设计方案。