Class-incremental learning (CIL) aims to enable models to continuously learn new classes while overcoming catastrophic forgetting. The introduction of pre-trained models has brought new tuning paradigms to CIL. In this paper, we revisit different parameter-efficient tuning (PET) methods within the context of continual learning. We observe that adapter tuning demonstrates superiority over prompt-based methods, even without parameter expansion in each learning session. Motivated by this, we propose incrementally tuning the shared adapter without imposing parameter update constraints, enhancing the learning capacity of the backbone. Additionally, we employ feature sampling from stored prototypes to retrain a unified classifier, further improving its performance. We estimate the semantic shift of old prototypes without access to past samples and update stored prototypes session by session. Our proposed method eliminates model expansion and avoids retaining any image samples. It surpasses previous pre-trained model-based CIL methods and demonstrates remarkable continual learning capabilities. Experimental results on five CIL benchmarks validate the effectiveness of our approach, achieving state-of-the-art (SOTA) performance.

翻译：类增量学习旨在使模型在持续学习新类别的同时克服灾难性遗忘。预训练模型的引入为类增量学习带来了新的调优范式。本文在持续学习背景下重新审视了不同的参数高效调优方法。我们观察到，即使在每个学习会话中不进行参数扩展，适配器调优也展现出优于提示方法的性能。受此启发，我们提出增量调优共享适配器而不施加参数更新约束，从而增强骨干网络的学习能力。此外，我们利用存储原型中的特征采样重新训练统一分类器，进一步提升了其性能。在无法访问过往样本的情况下，我们估计旧原型的语义偏移，并逐会话更新存储的原型。所提方法消除了模型扩展需求，避免了保留任何图像样本，在超越先前基于预训练模型类增量学习方法的同时，展现出卓越的持续学习能力。在五个类增量学习基准上的实验结果验证了该方法的有效性，达到了最先进的性能水平。