# Simplified Neural Unsupervised Domain Adaptation

arXiv: https://arxiv.org/abs/1905.09153 · HTML: https://mlsanity.com/paper/1905.09153/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 May 2019.

## Built on by (most cited first, 10 of 10)

- [UDALM: Unsupervised Domain Adaptation through Language Modeling](https://mlsanity.com/paper/2104.07078/) (2021)
- [Bayesian Attention Belief Networks](https://mlsanity.com/paper/2106.05251/) (2021)
- [Student Surpasses Teacher: Imitation Attack for Black-Box NLP APIs](https://mlsanity.com/paper/2108.13873/) (2021)
- [Alignment Attention by Matching Key and Query Distributions](https://mlsanity.com/paper/2110.12567/) (2021)
- [DILBERT: Customized Pre-Training for Domain Adaptation withCategory Shift, with an Application to Aspect Extraction](https://mlsanity.com/paper/2109.00571/) (2021)
- [Domain Confused Contrastive Learning for Unsupervised Domain Adaptation](https://mlsanity.com/paper/2207.04564/) (2022)
- [Tissue Segmentation of Thick-Slice Fetal Brain MR Scans with Guidance from High-Quality Isotropic Volumes](https://mlsanity.com/paper/2308.06762/) (2023)
- [Predicting the Success of Domain Adaptation in Text Similarity](https://mlsanity.com/paper/2106.04641/) (2021)
- [Domain Adaptation for Sparse-Data Settings: What Do We Gain by Not Using Bert?](https://mlsanity.com/paper/2203.16926/) (2022)
- [Causal Discovery Inspired Unsupervised Domain Adaptation for Emotion-Cause Pair Extraction](https://mlsanity.com/paper/2406.15490/) (2024)

## Abstract (arXiv)

Unsupervised domain adaptation (UDA) is the task of modifying a statistical model trained on labeled data from a source domain to achieve better performance on data from a target domain, with access to only unlabeled data in the target domain. Existing state-of-the-art UDA approaches use neural networks to learn representations that can predict the values of subset of important features called "pivot features." In this work, we show that it is possible to improve on these methods by jointly training the representation learner with the task learner, and examine the importance of existing pivot selection methods.