# Unsupervised Domain Adaptation with Progressive Domain Augmentation

arXiv: https://arxiv.org/abs/2004.01735 · HTML: https://mlsanity.com/paper/2004.01735/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Apr 2020.

## Built on by (most cited first, 2 of 2)

- [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)

Domain adaptation aims to exploit a label-rich source domain for learning classifiers in a different label-scarce target domain. It is particularly challenging when there are significant divergences between the two domains. In the paper, we propose a novel unsupervised domain adaptation method based on progressive domain augmentation. The proposed method generates virtual intermediate domains via domain interpolation, progressively augments the source domain and bridges the source-target domain divergence by conducting multiple subspace alignment on the Grassmann manifold. We conduct experiments on multiple domain adaptation tasks and the results shows the proposed method achieves the state-of-the-art performance.