What and Where: Learn to Plug Adapters via NAS for Multidomain Learning

Hanbin Zhao, Hao Zeng, Xin Qin, Yongjian Fu, Hui Wang, Bourahla Omar, Xi Li

2021IEEE Transactions on Neural Networks and Learning Systems20 citationsDOI

Abstract

As an important and challenging problem, multidomain learning (MDL) typically seeks a set of effective lightweight domain-specific adapter modules plugged into a common domain-agnostic network. Usually, existing ways of adapter plugging and structure design are handcrafted and fixed for all domains before model learning, resulting in learning inflexibility and computational intensiveness. With this motivation, we propose to learn a data-driven adapter plugging strategy with neural architecture search (NAS), which automatically determines where to plug for those adapter modules. Furthermore, we propose an NAS-adapter module for adapter structure design in an NAS-driven learning scheme, which automatically discovers effective adapter module structures for different domains. Experimental results demonstrate the effectiveness of our MDL model against existing approaches under the conditions of comparable performance.

Topics & Concepts

Adapter (computing)Computer scienceArchitectureArtificial intelligenceOperating systemArtVisual artsDomain Adaptation and Few-Shot LearningMultimodal Machine Learning ApplicationsAdvanced Neural Network Applications