# Unsupervised Domain Adaptation using Graph Transduction Games

arXiv: https://arxiv.org/abs/1905.02036 · HTML: https://mlsanity.com/paper/1905.02036/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 May 2019.

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

- [A Multi-stage Framework with Mean Subspace Computation and Recursive Feedback for Online Unsupervised Domain Adaptation](https://mlsanity.com/paper/2207.00003/) (2022)
- [UBR2S: Uncertainty-Based Resampling and Reweighting Strategy for Unsupervised Domain Adaptation](https://mlsanity.com/paper/2110.11739/) (2021)
- [Certainty Volume Prediction for Unsupervised Domain Adaptation](https://mlsanity.com/paper/2111.02901/) (2021)

## Abstract (arXiv)

Unsupervised domain adaptation (UDA) amounts to assigning class labels to the unlabeled instances of a dataset from a target domain, using labeled instances of a dataset from a related source domain. In this paper, we propose to cast this problem in a game-theoretic setting as a non-cooperative game and introduce a fully automatized iterative algorithm for UDA based on graph transduction games (GTG). The main advantages of this approach are its principled foundation, guaranteed termination of the iterative algorithms to a Nash equilibrium (which corresponds to a consistent labeling condition) and soft labels quantifying the uncertainty of the label assignment process. We also investigate the beneficial effect of using pseudo-labels from linear classifiers to initialize the iterative process. The performance of the resulting methods is assessed on publicly available object recognition benchmark datasets involving both shallow and deep features. Results of experiments demonstrate the suitability of the proposed game-theoretic approach for solving UDA tasks.