Collaborative Adapter Experts for Class-Incremental Learning

Pre-trained models (PTMs) with parameter-efficient fine-tuning (PEFT) techniques have been extensively utilized in class-incremental learning (CIL) scenarios. However, they still remain susceptible to performance degradation as the individual PEFT module operates as an independent learning entity during the incremental process. To this end, this work proposes a novel class-incremental collaborative adapter experts (CICAE) model, which incorporates multiple adapters operating collaboratively to facilitate CIL. Specifically, our model primarily consists of two phases. Initially, multiple adapters are employed to establish a multi-expert system aimed at acquiring diverse incremental knowledge. Through the collaborative knowledge sharing (CKS) mechanism, the expertise of each adapter expert is transferable, promoting collaborative development and mutual advancement. Subsequently, with the category prototype distributions, collaborative classifier alignment (CCA) is proposed to further align the classifiers with the representation space in a cooperative manner. Extensive experiments on CIL benchmarks validate the superior performance of our model.