  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.
