DualPrompt: Complementary Prompting for Rehearsal-free Continual Learning

• URL: http://arxiv.org/abs/2204.04799v1
• Date: Sun, 10 Apr 2022 23:36:55 GMT
• Title: DualPrompt: Complementary Prompting for Rehearsal-free Continual Learning
• Authors: Zifeng Wang, Zizhao Zhang, Sayna Ebrahimi, Ruoxi Sun, Han Zhang, Chen-Yu Lee, Xiaoqi Ren, Guolong Su, Vincent Perot, Jennifer Dy, Tomas Pfister
• Abstract summary: Continual learning aims to enable a single model to learn a sequence of tasks without catastrophic forgetting. We present DualPrompt, which learns a tiny set of parameters, called prompts, to instruct a pre-trained model to learn tasks arriving sequentially. With extensive experimental validation, DualPrompt consistently sets state-of-the-art performance under the challenging class-incremental setting.
• Score: 39.53513975439818
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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 https://github.com/google-research/l2p.

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