Continual learning requires the model to learn multiple tasks sequentially. In continual learning, the model should possess the ability to maintain its performance on old tasks (stability) and the ability to adapt to new tasks continuously (plasticity). Recently, parameter-efficient fine-tuning (PEFT), which involves freezing a pre-trained model and injecting a small number of learnable parameters to adapt to downstream tasks, has gained increasing popularity in continual learning. Although existing continual learning methods based on PEFT have demonstrated superior performance compared to those not based on PEFT, most of them do not consider how to eliminate the interference of the new task on the old tasks, which inhibits the model from making a good trade-off between stability and plasticity. In this work, we propose a new PEFT method, called interference-free low-rank adaptation (InfLoRA), for continual learning. InfLoRA injects a small number of parameters to reparameterize the pre-trained weights and shows that fine-tuning these injected parameters is equivalent to fine-tuning the pre-trained weights within a subspace. Furthermore, InfLoRA designs this subspace to eliminate the interference of the new task on the old tasks, making a good trade-off between stability and plasticity. Experimental results show that InfLoRA outperforms existing state-of-the-art continual learning methods on multiple datasets.

翻译：持续学习要求模型能够顺序学习多个任务。在持续学习中，模型应具备维持旧任务性能的能力（稳定性）以及持续适应新任务的能力（可塑性）。近年来，参数高效微调（PEFT）方法——即冻结预训练模型并注入少量可学习参数以适应下游任务——在持续学习领域日益流行。尽管现有基于PEFT的持续学习方法展现出优于非PEFT方法的性能，但大多数方法并未考虑如何消除新任务对旧任务的干扰，这阻碍了模型在稳定性和可塑性之间取得良好平衡。本文提出一种面向持续学习的新型PEFT方法，称为无干扰低秩适应（InfLoRA）。InfLoRA注入少量参数对预训练权重进行重参数化，并证明微调这些注入参数等价于在子空间内微调预训练权重。此外，InfLoRA通过设计该子空间来消除新任务对旧任务的干扰，从而在稳定性和可塑性之间实现良好权衡。实验结果表明，InfLoRA在多个数据集上优于现有最先进的持续学习方法。