# Towards Fair Cross-Domain Adaptation via Generative Learning

arXiv: https://arxiv.org/abs/2003.02366 · HTML: https://mlsanity.com/paper/2003.02366/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Mar 2020.

## Built on by (most cited first, 8 of 8)

- [Transferring Fairness under Distribution Shifts via Fair Consistency Regularization](https://mlsanity.com/paper/2206.12796/) (2022)
- [Cross-Site Severity Assessment of COVID-19 from CT Images via Domain Adaptation](https://mlsanity.com/paper/2109.03478/) (2021)
- [Fairness in Visual Clustering: A Novel Transformer Clustering Approach](https://mlsanity.com/paper/2304.07408/) (2023)
- [Fair Bilevel Neural Network (FairBiNN): On Balancing fairness and accuracy via Stackelberg Equilibrium](https://mlsanity.com/paper/2410.16432/) (2024)
- [Through a fair looking-glass: mitigating bias in image datasets](https://mlsanity.com/paper/2209.08648/) (2022)
- [Distraction is All You Need for Fairness](https://mlsanity.com/paper/2203.07593/) (2022)
- [Data-Efficient CLIP-Powered Dual-Branch Networks for Source-Free Unsupervised Domain Adaptation](https://mlsanity.com/paper/2410.15811/) (2024)
- [Trustworthy Representation Learning Across Domains](https://mlsanity.com/paper/2308.12315/) (2023)

## Abstract (arXiv)

Domain Adaptation (DA) targets at adapting a model trained over the well-labeled source domain to the unlabeled target domain lying in different distributions. Existing DA normally assumes the well-labeled source domain is class-wise balanced, which means the size per source class is relatively similar. However, in real-world applications, labeled samples for some categories in the source domain could be extremely few due to the difficulty of data collection and annotation, which leads to decreasing performance over target domain on those few-shot categories. To perform fair cross-domain adaptation and boost the performance on these minority categories, we develop a novel Generative Few-shot Cross-domain Adaptation (GFCA) algorithm for fair cross-domain classification. Specifically, generative feature augmentation is explored to synthesize effective training data for few-shot source classes, while effective cross-domain alignment aims to adapt knowledge from source to facilitate the target learning. Experimental results on two large cross-domain visual datasets demonstrate the effectiveness of our proposed method on improving both few-shot and overall classification accuracy comparing with the state-of-the-art DA approaches.