# Budget-Aware Adapters for Multi-Domain Learning

arXiv: https://arxiv.org/abs/1905.06242 · HTML: https://mlsanity.com/paper/1905.06242/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 May 2019.

## Built on by (most cited first, 18 of 18)

- [Three things everyone should know about Vision Transformers](https://mlsanity.com/paper/2203.09795/) (2022)
- [Modular Deep Learning](https://mlsanity.com/paper/2302.11529/) (2023)
- [Task Adaptive Parameter Sharing for Multi-Task Learning](https://mlsanity.com/paper/2203.16708/) (2022)
- [KG-SP: Knowledge Guided Simple Primitives for Open World Compositional Zero-Shot Learning](https://mlsanity.com/paper/2205.06784/) (2022)
- [On the Effectiveness of LayerNorm Tuning for Continual Learning in Vision Transformers](https://mlsanity.com/paper/2308.09610/) (2023)
- [Mini but Mighty: Finetuning ViTs with Mini Adapters](https://mlsanity.com/paper/2311.03873/) (2023)
- [Boosting Binary Masks for Multi-Domain Learning through Affine Transformations](https://mlsanity.com/paper/2103.13894/) (2021)
- [Few-shot Semantic Image Synthesis with Class Affinity Transfer](https://mlsanity.com/paper/2304.02321/) (2023)
- [Disentangling Transfer and Interference in Multi-Domain Learning](https://mlsanity.com/paper/2107.05445/) (2021)
- [Your representations are in the network: composable and parallel adaptation for large scale models](https://mlsanity.com/paper/2303.04105/) (2023)
- [An Empirical Study on Multi-Domain Robust Semantic Segmentation](https://mlsanity.com/paper/2212.04221/) (2022)
- [Budget-Aware Pruning for Multi-Domain Learning](https://mlsanity.com/paper/2210.08101/) (2022)
- [Multi-Domain Learning with Modulation Adapters](https://mlsanity.com/paper/2307.08528/) (2023)
- [Parameter-efficient Multi-Task and Multi-Domain Learning using Factorized Tensor Networks](https://mlsanity.com/paper/2310.06124/) (2023)
- [InterroGate: Learning to Share, Specialize, and Prune Representations for Multi-task Learning](https://mlsanity.com/paper/2402.16848/) (2024)
- [Unidirectional Thin Adapter for Efficient Adaptation of Deep Neural Networks](https://mlsanity.com/paper/2203.10463/) (2022)
- [Budget-Aware Pruning: Handling Multiple Domains with Less Parameters](https://mlsanity.com/paper/2309.11464/) (2023)
- [On the Importance of Pretraining Data Alignment for Atomic Property Prediction](https://mlsanity.com/paper/2502.11085/) (2025)

## Abstract (arXiv)

Multi-Domain Learning (MDL) refers to the problem of learning a set of models derived from a common deep architecture, each one specialized to perform a task in a certain domain (e.g., photos, sketches, paintings). This paper tackles MDL with a particular interest in obtaining domain-specific models with an adjustable budget in terms of the number of network parameters and computational complexity. Our intuition is that, as in real applications the number of domains and tasks can be very large, an effective MDL approach should not only focus on accuracy but also on having as few parameters as possible. To implement this idea we derive specialized deep models for each domain by adapting a pre-trained architecture but, differently from other methods, we propose a novel strategy to automatically adjust the computational complexity of the network. To this aim, we introduce Budget-Aware Adapters that select the most relevant feature channels to better handle data from a novel domain. Some constraints on the number of active switches are imposed in order to obtain a network respecting the desired complexity budget. Experimentally, we show that our approach leads to recognition accuracy competitive with state-of-the-art approaches but with much lighter networks both in terms of storage and computation.