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What and Where: Learn to Plug Adapters via NAS for Multidomain Learning
Summary
This study introduces a novel approach to multidomain learning (MDL) using neural architecture search (NAS) to automatically design adapter modules and their placement, improving learning flexibility and efficiency.
Area of Science:
- Artificial Intelligence
- Machine Learning
- Computer Science
Background:
- Multidomain learning (MDL) aims to create effective, lightweight domain-specific adapter modules for common networks.
- Current methods use fixed, handcrafted adapter designs, limiting flexibility and increasing computational load.
Purpose of the Study:
- To develop a data-driven approach for determining adapter module placement using neural architecture search (NAS).
- To automatically discover optimal adapter module structures for diverse domains via an NAS-adapter module.
Main Methods:
- Implemented a neural architecture search (NAS) strategy to learn optimal adapter plugging points.
- Introduced an NAS-adapter module to automatically design adapter structures within an NAS-driven learning framework.
Main Results:
- The proposed MDL model demonstrates effectiveness compared to existing methods.
- Achieved comparable performance with improved learning flexibility and reduced computational intensiveness.
Conclusions:
- The NAS-driven approach offers a more flexible and efficient solution for multidomain learning.
- Automating adapter design and placement through NAS significantly enhances MDL model performance.
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