Improving Prompt-based Continual Learning with Key-Query Orthogonal Projection and Prototype-based One-Versus-All

Quyen Tran, Tung Lam Tran, Khoat Than, Toan Tran, Dinh Phung, Trung Le

Keywords: continual learning, prompt tuning, ViT

Abstract: Drawing inspiration from prompt tuning techniques applied to Large Language Models, recent methods based on pre-trained ViT networks have achieved remarkable results in the field of Continual Learning. Specifically, these approaches propose to maintain a set of prompts and allocate a subset of them to learn each task using a key-query matching strategy. However, they may encounter limitations when lacking control over the shift of features in the latent space and the relative separation of latent vectors learned in independent tasks. In this work, we introduce a novel key-query learning strategy based on orthogonal projection, inspired by model-agnostic meta-learning, to enhance prompt matching efficiency and address the challenge of shifting features. Furthermore, to harness the benefits of reduced feature shifting, we introduce a One-Versus-All (OVA) prototype-based component that enhances the performance of the classification head. Experimental results on benchmark datasets demonstrate that our method empowers the model to achieve results surpassing those of current state-of-the-art approaches by a large margin of up to 20%.

Primary Area: transfer learning, meta learning, and lifelong learning

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Submission Number: 7311