DuPt: Rehearsal-based continual learning with dual prompts

Abstract

The rehearsal-based continual learning methods usually involve reviewing a small number of representative samples to enable the network to learn new contents while retaining old knowledge. However, existing works overlook two crucial factors: (1) While the network prioritizes learning new data at incremental stages, it exhibits weaker generalization capabilities when trained individually on limited samples from specific categories, in contrast to training on large-scale samples across multiple categories simultaneously. (2) Knowledge distillation of a limited set of old samples can transfer certain existing knowledge, but imposing strong constraints may hinder knowledge transfer and restrict the ability of the network from the current stage to capture fresh knowledge. To alleviate these issues, we propose a rehearsal-based continual learning method with dual prompts, termed DuPt. First, we propose an input-aware prompt, an input-level cue that utilizes an input prior to querying for valid cue information. These hints serve as an additional complement to help the input samples generate more rational and diverse distributions. Second, we introduce a proxy feature prompt, a feature-level hint that bridges the knowledge gap between the teacher and student models to maintain consistency in the feature transfer process, reinforcing feature plasticity and stability. This is because differences in network features between the new and old incremental stages could affect the generalization of their new models if strictly aligned. Our proposed prompt can act as a consistency regularization to avoid feature conflicts caused by the differences between network features. Extensive experiments validate the effectiveness of our method, which can seamlessly integrate with existing methods, leading to performance improvements.