Adapters present a promising solution to the catastrophic forgetting problem in continual learning. However, training independent Adapter modules for every new task misses an opportunity for cross-task knowledge transfer. We propose Improvise to Initialize (I2I), a continual learning algorithm that initializes Adapters for incoming tasks by distilling knowledge from previously-learned tasks' Adapters. We evaluate I2I on CLiMB, a multimodal continual learning benchmark, by conducting experiments on sequences of visual question answering tasks. Adapters trained with I2I consistently achieve better task accuracy than independently-trained Adapters, demonstrating that our algorithm facilitates knowledge transfer between task Adapters. I2I also results in better cross-task knowledge transfer than the state-of-the-art AdapterFusion without incurring the associated parametric cost.

翻译：摘要：适配器为持续学习中的灾难性遗忘问题提供了一种有前景的解决方案。然而，为每个新任务训练独立的适配器模块会错失跨任务知识迁移的机会。我们提出即兴初始化（I2I）算法，该算法通过从先前学习任务的适配器中蒸馏知识，为传入任务初始化适配器。我们在多模态持续学习基准CLiMB上，针对一系列视觉问答任务进行实验评估I2I。使用I2I训练的适配器在任务准确率上始终优于独立训练的适配器，表明我们的算法促进了任务适配器间的知识迁移。与现有最先进的AdapterFusion相比，I2I实现了更好的跨任务知识迁移，且无需承担相应的参数成本。