# What and Where: Learn to Plug Adapters via NAS for Multi-Domain Learning

arXiv: https://arxiv.org/abs/2007.12415 · HTML: https://mlsanity.com/paper/2007.12415/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Jul 2020.

## Built on by (most cited first, 7 of 7)

- [RBC: Rectifying the Biased Context in Continual Semantic Segmentation](https://mlsanity.com/paper/2203.08404/) (2022)
- [IAP: Improving Continual Learning of Vision-Language Models via Instance-Aware Prompting](https://mlsanity.com/paper/2503.20612/) (2025)
- [Rethinking Hard-Parameter Sharing in Multi-Domain Learning](https://mlsanity.com/paper/2107.11359/) (2021)
- [Towards Optimal Adapter Placement for Efficient Transfer Learning](https://mlsanity.com/paper/2410.15858/) (2024)
- [GPPF: A General Perception Pre-training Framework via Sparsely Activated Multi-Task Learning](https://mlsanity.com/paper/2208.02148/) (2022)
- [Parameter-efficient Multi-Task and Multi-Domain Learning using Factorized Tensor Networks](https://mlsanity.com/paper/2310.06124/) (2023)
- [Cascaded Multi-task Adaptive Learning Based on Neural Architecture Search](https://mlsanity.com/paper/2310.17664/) (2023)

## Abstract (arXiv)

As an important and challenging problem, multi-domain learning (MDL) typically seeks for 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 the 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 a NAS-adapter module for adapter structure design in a 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.