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arXiv · 2304.02168

I2I: Initializing Adapters with Improvised Knowledge

Tejas Srinivasan·Furong Jia·Mohammad Rostami·Jesse Thomason
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Abstract

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.

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Tejas Srinivasan, Furong Jia, Mohammad Rostami, Jesse Thomason. 2023-04-04. I2I: Initializing Adapters with Improvised Knowledge. https://arxiv.org/abs/2304.02168

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