The mainstream paradigm behind continual learning has been to adapt the model
parameters to non-stationary data distributions, where catastrophic forgetting
is the central challenge. Typical methods rely on a rehearsal buffer or known
task identity at test time to retrieve learned knowledge and address
forgetting, while this work presents a new paradigm for continual learning that
aims to train a more succinct memory system without accessing task identity at
test time. Our method learns to dynamically prompt (L2P) a pre-trained model to
learn tasks sequentially under different task transitions. In our proposed
framework, prompts are small learnable parameters, which are maintained in a
memory space. The objective is to optimize prompts to instruct the model
prediction and explicitly manage task-invariant and task-specific knowledge
while maintaining model plasticity. We conduct comprehensive experiments under
popular image classification benchmarks with different challenging continual
learning settings, where L2P consistently outperforms prior state-of-the-art
methods. Surprisingly, L2P achieves competitive results against rehearsal-based
methods even without a rehearsal buffer and is directly applicable to
challenging task-agnostic continual learning. Source code is available at
this https URL

提出了利用动态提示 (L2P) 来训练更简洁的神经网络内存系统的方法，以管理任务不变和任务特定知识，从而在各种挑战性的图像分类基准测试中取得了比之前最先进方法更好的结果。