# Multi-step Online Unsupervised Domain Adaptation

arXiv: https://arxiv.org/abs/2002.08930 · HTML: https://mlsanity.com/paper/2002.08930/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Feb 2020.

## Built on by (most cited first, 7 of 7)

- [A Comprehensive Survey on Test-Time Adaptation under Distribution Shifts](https://mlsanity.com/paper/2303.15361/) (2023)
- [GIPSO: Geometrically Informed Propagation for Online Adaptation in 3D LiDAR Segmentation](https://mlsanity.com/paper/2207.09763/) (2022)
- [Burn After Reading: Online Adaptation for Cross-domain Streaming Data](https://mlsanity.com/paper/2112.04345/) (2021)
- [A Multi-stage Framework with Mean Subspace Computation and Recursive Feedback for Online Unsupervised Domain Adaptation](https://mlsanity.com/paper/2207.00003/) (2022)
- [Online unsupervised Learning for domain shift in COVID-19 CT scan datasets](https://mlsanity.com/paper/2108.02002/) (2021)
- [Self-supervised Learning of 3D Object Understanding by Data Association and Landmark Estimation for Image Sequence](https://mlsanity.com/paper/2104.07077/) (2021)
- [Real-Time Online Unsupervised Domain Adaptation for Real-World Person Re-identification](https://mlsanity.com/paper/2306.03993/) (2023)

## Abstract (arXiv)

In this paper, we address the Online Unsupervised Domain Adaptation (OUDA) problem, where the target data are unlabelled and arriving sequentially. The traditional methods on the OUDA problem mainly focus on transforming each arriving target data to the source domain, and they do not sufficiently consider the temporal coherency and accumulative statistics among the arriving target data. We propose a multi-step framework for the OUDA problem, which institutes a novel method to compute the mean-target subspace inspired by the geometrical interpretation on the Euclidean space. This mean-target subspace contains accumulative temporal information among the arrived target data. Moreover, the transformation matrix computed from the mean-target subspace is applied to the next target data as a preprocessing step, aligning the target data closer to the source domain. Experiments on four datasets demonstrated the contribution of each step in our proposed multi-step OUDA framework and its performance over previous approaches.