DualPrompt: Complementary Prompting for Rehearsal-Free Continual Learning

• Published in: Computer Vision - ECCV 2022 Vol. 13686; pp. 631 - 648
• Main Authors: Wang, Zifeng, Zhang, Zizhao, Ebrahimi, Sayna, Sun, Ruoxi, Zhang, Han, Lee, Chen-Yu, Ren, Xiaoqi, Su, Guolong, Perot, Vincent, Dy, Jennifer, Pfister, Tomas
• Format: Book Chapter
• Language: English
• Published: Switzerland Springer 2022; Springer Nature Switzerland
• Series: Lecture Notes in Computer Science
• Subjects: Continual learning; Prompt-based learning; Rehearsal-free
• ISBN: 9783031198083; 3031198085
• ISSN: 0302-9743; 1611-3349
• DOI: 10.1007/978-3-031-19809-0_36

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.