# Multi-source Attention for Unsupervised Domain Adaptation

arXiv: https://arxiv.org/abs/2004.06608 · HTML: https://mlsanity.com/paper/2004.06608/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Apr 2020.

## Built on by (most cited first, 3 of 3)

- [FRuDA: Framework for Distributed Adversarial Domain Adaptation](https://mlsanity.com/paper/2112.13381/) (2021)
- [Multiple-Source Domain Adaptation via Coordinated Domain Encoders and Paired Classifiers](https://mlsanity.com/paper/2201.11870/) (2022)
- [Open-Set Multi-Source Multi-Target Domain Adaptation](https://mlsanity.com/paper/2302.00995/) (2023)

## Abstract (arXiv)

Domain adaptation considers the problem of generalising a model learnt using data from a particular source domain to a different target domain. Often it is difficult to find a suitable single source to adapt from, and one must consider multiple sources. Using an unrelated source can result in sub-optimal performance, known as the negative transfer. However, it is challenging to select the appropriate source(s) for classifying a given target instance in multi-source unsupervised domain adaptation (UDA). We model source-selection as an attention-learning problem, where we learn attention over sources for a given target instance. For this purpose, we first independently learn source-specific classification models, and a relatedness map between sources and target domains using pseudo-labelled target domain instances. Next, we learn attention-weights over the sources for aggregating the predictions of the source-specific models. Experimental results on cross-domain sentiment classification benchmarks show that the proposed method outperforms prior proposals in multi-source UDA.