Continual learning aims to enable a single model to learn a sequence of tasks
without catastrophic forgetting. Top-performing methods usually require a
rehearsal buffer to store past pristine examples for experience replay, which,
however, limits their practical value due to privacy and memory constraints. In
this work, we present a simple yet effective framework, DualPrompt, which
learns a tiny set of parameters, called prompts, to properly instruct a
pre-trained model to learn tasks arriving sequentially without buffering past
examples. DualPrompt presents a novel approach to attach complementary prompts
to the pre-trained backbone, and then formulates the objective as learning
task-invariant and task-specific "instructions". With extensive experimental
validation, DualPrompt consistently sets state-of-the-art performance under the
challenging class-incremental setting. In particular, DualPrompt outperforms
recent advanced continual learning methods with relatively large buffer sizes.
We also introduce a more challenging benchmark, Split ImageNet-R, to help
generalize rehearsal-free continual learning research. Source code is available
at this https URL

DualPrompt 提出了一种无需 rehearsal buffer 的连续学习框架，通过学习使用预先训练好的模型进行有序任务学习的任务不变和任务特定的指示来达到目的，表现出卓越的性能。