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.

通过注入少量参数重参数化预训练权重，InfLoRA 方法设计了一个子空间来消除新任务对旧任务的干扰，实现了稳定性和可塑性之间的良好权衡，从而在多个数据集上胜过现有最先进的持续学习方法。